diff --git a/examples/causal_inference/interventional_distribution.ipynb b/examples/causal_inference/interventional_distribution.ipynb new file mode 100644 index 000000000..7c6621d0e --- /dev/null +++ b/examples/causal_inference/interventional_distribution.ipynb @@ -0,0 +1,1917 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f2e8530c-5ba0-4041-a309-18919d5d0533", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "(interventional_distribution)=\n", + "# Interventional distributions and graph mutilation with the do-operator\n", + "\n", + ":::{post} July, 2023\n", + ":tags: causal inference, do-operator, graph mutation\n", + ":category: beginner, explanation\n", + ":author: Benjamin T. Vincent\n", + ":::" + ] + }, + { + "cell_type": "markdown", + "id": "8f973a4f-def4-4fb6-b166-2aefbdf5eee2", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "[PyMC](https://github.com/pymc-devs/pymc) is a pivotal component of the open source Bayesian statistics ecosystem. It helps solve real problems across a wide range of industries and academic research areas every day. And it has gained this level of utility by being accessible, powerful, and practically useful at solving _Bayesian statistical inference_ problems.\n", + "\n", + "But times are changing. There's a [causal revolution](https://en.wikipedia.org/wiki/The_Book_of_Why) underway and there's a growing recognition that to answer some of the most interesting and challenging questions requires us to intergrate causal reasoning into our efforts.\n", + "\n", + "PyMC is rising to this challenge! While there are many novel causal concepts to learn, Bayesians will find that they are not starting from scratch. They are already pretty familiar with [Directed Acyclic Graphs (DAGs)](https://en.wikipedia.org/wiki/Directed_acyclic_graph) and so this gives a good jumping off point to gain relatively easy access into the world of **Bayesian causal inference**.\n", + "\n", + "This notebook is going to cover one of the foundational components of causal reasoning which has newly arrived to the PyMC ecosystem, the $\\operatorname{do}$ operator. Indeed, depending on whose definitions you want to use, adding the $\\operatorname{do}$ operator into the kind of Bayesian DAGs that PyMC users build every day gets us to the status of building [causal Bayesian networks](https://en.wikipedia.org/wiki/Causal_graph). \n", + "\n", + "If that sounds cool, let's dive in..." + ] + }, + { + "cell_type": "markdown", + "id": "ed816fbf-320c-44f7-a711-cb4750d86e24", + "metadata": {}, + "source": [ + "## Set up the notebook" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "efb41c68-2dbc-4f70-b333-eef4c743994a", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import arviz as az\n", + "import graphviz as gr\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pymc as pm\n", + "import seaborn as sns\n", + "\n", + "from packaging import version" + ] + }, + { + "cell_type": "markdown", + "id": "e2441116-c8a5-41af-be95-9eeddf2b0e2e", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + ":::{include} ../extra_installs.md\n", + ":::\n", + "\n", + "This notebook relies on experimental functionality currently in the [pymc-experimental](https://github.com/pymc-devs/pymc-experimental) repository. In the near future this will be moved into the main [pymc](https://github.com/pymc-devs/pymc) repository." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "70a77819-b114-49ca-960e-a8bb504d6af9", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# Import additional libraries that are not dependencies of PyMC\n", + "import daft\n", + "import pymc_experimental as pmx\n", + "\n", + "# Check we have the necessary versions to get the new experimental functionality.\n", + "assert version.parse(pm.__version__) >= version.parse(\"5.5.0\")\n", + "assert version.parse(pmx.__version__) >= version.parse(\"0.0.7\")\n", + "\n", + "# import the new functionality\n", + "from pymc_experimental.model_transform.conditioning import do" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "5403941e-6a30-4f93-8533-e219805b2c3c", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "RANDOM_SEED = 123\n", + "rng = np.random.default_rng(RANDOM_SEED)\n", + "az.style.use(\"arviz-darkgrid\")\n", + "%config InlineBackend.figure_format = 'retina'" + ] + }, + { + "cell_type": "markdown", + "id": "782d6135-2e59-4462-8b9c-9832a58751ef", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## What can we do with Bayesian inference?\n", + "\n", + "Whether we are building _descriptive_ models or those that try to model the underlying processes, Bayesians are very used to building white box (i.e. the opposite of [black box](https://en.wikipedia.org/wiki/Black_box)), interpretable, models of [data generating processes](https://en.wikipedia.org/wiki/Data_generating_process). While we construct PyMC models using code, behind the scenes this is represented as a DAG, which we can visualise with graphviz. Let's see how this works using the example in the docs:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b770f730-31a2-42c2-b269-5a0f2091538c", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "cluster8\n", + "\n", + "8\n", + "\n", + "\n", + "\n", + "eta\n", + "\n", + "eta\n", + "~\n", + "Normal\n", + "\n", + "\n", + "\n", + "obs\n", + "\n", + "obs\n", + "~\n", + "Normal\n", + "\n", + "\n", + "\n", + "eta->obs\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "mu\n", + "\n", + "mu\n", + "~\n", + "Normal\n", + "\n", + "\n", + "\n", + "mu->obs\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "tau\n", + "\n", + "tau\n", + "~\n", + "HalfCauchy\n", + "\n", + "\n", + "\n", + "tau->obs\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "J = 8\n", + "y = np.array([28, 8, -3, 7, -1, 1, 18, 12])\n", + "sigma = np.array([15, 10, 16, 11, 9, 11, 10, 18])\n", + "\n", + "with pm.Model() as schools:\n", + " eta = pm.Normal(\"eta\", 0, 1, shape=J)\n", + " mu = pm.Normal(\"mu\", 0, sigma=1e6)\n", + " tau = pm.HalfCauchy(\"tau\", 25)\n", + " theta = mu + tau * eta\n", + " obs = pm.Normal(\"obs\", theta, sigma=sigma, observed=y)\n", + "\n", + "pm.model_to_graphviz(schools)" + ] + }, + { + "cell_type": "markdown", + "id": "f618f053-266b-4006-a521-f892cb8519e1", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Regardless of the particular model or models we are working with, we can do a whole range of _statistical_ procedures:\n", + "\n", + "* We could examine the [prior predictive distribution](https://en.wikipedia.org/wiki/Posterior_predictive_distribution#Prior_vs._posterior_predictive_distribution) to see what we'd expect to see in the given DAG based on our stated prior beliefs with `pymc.sample_prior_predictive`. An example use case would be when we want to understand our predictions of how inflation may evolve into the future based on the structure of our model (e.g. the national and international economy) and our prior beliefs over latent variables.\n", + "* We could conduct Bayesian inference by sampling from the posterior distribution with `pymc.sample`. This would update our beliefs to assign credibility to different values of latent variables given the data that we have observed. For example, maybe we get another inflation data point added to our dataset and we want to update our beliefs about the latent variables in the model of the economy.\n", + "* We could examine the [posterior predictive distribution](https://en.wikipedia.org/wiki/Posterior_predictive_distribution) using `pymc.sample_posterior_predictive`. This is closely related to the prior predictive distribution, but in our running example it would allow us to create a revised set of predictions about future inflation rates after we've observed another data point.\n", + "* If we wanted, we could get fancy with {ref}`GLM-model-selection` to compare different models (data generating processes). This could be particularly useful because we arguably don't have complete faith that we know the \"true\" model of the economy, even at a coarse level of abstraction. So we could build multiple models (DAGs) and evaluate the relative credibility that each model generated the observed data.\n", + "* If we have a number of candidate data generating processes, we could incorporate our uncertainty in the data generating process through {ref}`model_averaging`.\n", + "\n", + "If we've mastered all of these steps, we can rightfully feel pretty happy with ourselves. We can accomplish a lot with these statistical and predictive procedures." + ] + }, + { + "cell_type": "markdown", + "id": "ca2ed504-ffda-4f1d-8d18-cdb06608202b", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hide-input" + ] + }, + "source": [ + "## Why causality is important\n", + "\n", + "But now it's time to get smacked in our smug Bayesian face. As others have argued (e.g. {cite:t}`pearl2018why,pearl2000causality`), it is entirely possible to build a pretty good _predictive_ model, but one which can catestrophically fail the moment you (or anyone else) intervenes in the system. Such interventions can totally destroy predictive modelling approaches and wake you up real fast to the necessity of adding causal reasoning into our skillset. \n", + "\n", + "In our running example, this could correspond to when a central bank switches from making predictions about inflation to now _acting_ and _intervening_ in the system by, for example, changing interest rates. All of a sudden you might be faced with a situation where the economy does not respond to your intervention as you predicted." + ] + }, + { + "cell_type": "markdown", + "id": "83b8106f-c9e1-4277-ba38-f13b03a33ef6", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Let's consider a seemingly trivial example with 3 nodes to see how we can get fooled. The image below shows two different causal DAGs. On the left we are interested in how $X$ causally affects $Y$, both directly and indirectly through a mediating variable $M$. If we take a purely statistical approach (e.g. {ref}`mediation_analysis`) we might find that the data is very plausibly generated by this DAG. This might give us the confidence to conduct an intervention on $M$ with the aim of influencing our target outcome, $Y$. But when we do this intervention in the real world and change $M$, we actually find absolutely no change in $Y$. What is going on here?" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "39bebf1f-b31b-4269-8251-42a7cfbd12a7", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "x2\n", + "\n", + "X\n", + "\n", + "\n", + "\n", + "y2\n", + "\n", + "Y\n", + "\n", + "\n", + "\n", + "x2->y2\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "m2\n", + "\n", + "M\n", + "\n", + "\n", + "\n", + "x2->m2\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "m2->y2\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "x\n", + "\n", + "X\n", + "\n", + "\n", + "\n", + "y\n", + "\n", + "Y\n", + "\n", + "\n", + "\n", + "x->y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "m\n", + "\n", + "M\n", + "\n", + "\n", + "\n", + "x->m\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "m->y\n", + "\n", + "\n", + "\n", + "\n", + "u\n", + "\n", + "U\n", + "\n", + "\n", + "\n", + "u->y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "u->m\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "g = gr.Digraph()\n", + "# Wrong data generating process\n", + "g.node(name=\"x2\", label=\"X\")\n", + "g.node(name=\"y2\", label=\"Y\")\n", + "g.node(name=\"m2\", label=\"M\")\n", + "g.edge(tail_name=\"x2\", head_name=\"y2\")\n", + "g.edge(tail_name=\"x2\", head_name=\"m2\")\n", + "g.edge(tail_name=\"m2\", head_name=\"y2\")\n", + "# Actual causal DAG\n", + "g.node(name=\"x\", label=\"X\")\n", + "g.node(name=\"y\", label=\"Y\")\n", + "g.node(name=\"m\", label=\"M\")\n", + "g.node(name=\"u\", label=\"U\", color=\"lightgrey\", style=\"filled\")\n", + "g.edge(tail_name=\"x\", head_name=\"y\")\n", + "g.edge(tail_name=\"x\", head_name=\"m\")\n", + "g.edge(tail_name=\"m\", head_name=\"y\", style=\"dashed\", dir=\"none\")\n", + "g.edge(tail_name=\"u\", head_name=\"m\", color=\"lightgrey\")\n", + "g.edge(tail_name=\"u\", head_name=\"y\", color=\"lightgrey\")\n", + "# Render\n", + "g" + ] + }, + { + "cell_type": "markdown", + "id": "56832ddc-ef61-4dbf-b46f-b6ef2ae8820e", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Little did we know, but the _actual_ data generating process is captured by the DAG on the right. This shows that $X$ does causally influence both $M$ and $Y$, however $M$ does not in fact causally affect $Y$. Instead, there is an unobserved variable $U$ which causally influences both $M$ and $Y$. This unobserved confounder creates a backdoor path in which _statistical_ association may flow between the path $X \\rightarrow M \\rightarrow U \\rightarrow Y$. All this causes a statistical association between $M$ and $Y$ which our purely statistical approach mislead us into thinking that $M$ did causally influence $Y$ when it did not. No wonder our intervention failed to have any effects.\n", + "\n", + "Our mistake was to interpret a statistical model causally." + ] + }, + { + "cell_type": "markdown", + "id": "24792fb8-3e40-41b5-a26a-4a2d4da052ed", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Statistical versus interventional distributions\n", + "So far this has been quite high-level, but let's try to pin this down a little. In our example, if we were to take a purely statistical approach we could ask \"What happened when interest rates were 2%?\" This is a statistical question because we are basically looking back in our dataset and filtering (or conditioning) upon time points where interest rates were at (or very close to) 2%. So let's flag up - **conditional distributions are purely statistical quantities**.\n", + "\n", + "Though the real question we might want an answer to is \"What would have happened in the past if we had set the interest rates to 2%?\" or \"What will happen going forward if we set the interest rates to 2%?\" Despite the subtle changing of wording, this now radically changes what we have to do in order to answer the question. So a key point here is **interventional distributions require causal (not statistical) approaches**.\n", + "\n", + "Interventional distributions are cool because they allow us to ask what-if (or counterfactual questions). For example, with a causal DAG we could ask questions of the form, \"What do I think will happen in the future if I do X?\" or \"What do I think would have happened in the past if X had happened?\" See how these types of questions have a very different flavour to purely statistical kinds of questions - they would be more like \"Given what I've seen, what do I think will happen.\" See how this has a more passive, observational focus.\n", + "\n", + "From hereon, the main point of this notebook will be to provide some understanding and intuition about the differences between conditional and interventional distributions, and how to estimate interventional distributions with PyMC. As we said above, we can use the $\\operatorname{do}$ operator to estimate interventional distributions. So let's dive in and see how that works." + ] + }, + { + "cell_type": "markdown", + "id": "aac4d49b-6a1e-4d77-a9c7-205050bebb9f", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Interventions and the $\\operatorname{do}$ operator\n", + "\n", + "We'll consider an example from {cite:t}`pearl2000causality` where we examine a DAG which is a putative causal explanation of how various factors influence each other to result in grass becoming slippery. The left shows our causal DAG, and the right shows how the DAG is changed if we consider an intervention (hypothetical or actual) where we turn the sprinkler on. The $\\operatorname{do}$ operator implements an intervention that we want to make. It consists of 2 simple steps:\n", + "\n", + "1. It takes a given node in a graph and sets that node at the desired value.\n", + "2. It removes any causal influence on this node by other nodes. It does this by removing all incoming edges into that node.\n", + "\n", + "![](sprinkler.png)\n", + "\n", + "On the left of the figure we have a causal DAG describing the causal relationships between season, whether a sprinkler has been on, whether it has rained, if the grass is wet, and if the grass is slippery. \n", + "\n", + "The joint distribution can be factorised as: \n", + "\n", + "$$\n", + "P(x_1, x_2, x_3, x_4, x_5) = P(x_1) P(x_3|x_1) P(x_2|x_1) P(x_4|x_3, x_2) P(x_5|x_4)\n", + "$$\n", + "\n", + "```{card} Factorizing joint distributions\n", + "For a DAG, a complex joint distribution can be broken down into the product of conditional distributions:\n", + "\n", + "$$\n", + "P(x_1, x_2, \\ldots, x_n) = \\prod_i P(x_i|pa_i)\n", + "$$\n", + "\n", + "where $pa_i$ are the parents of node $x_i$, and $i = \\{ 1, \\ldots, n \\}$.\n", + "```\n", + "\n", + "On the right of the figure we have applied the $\\operatorname{do}$ operator to examine what will happen if we set the sprinkler to be on. You can see that we have now set the value of that node, $x_3=1$ and we have removed the incoming edge (influence) of season, meaning that once we turn on the sprinkler manually, it's not influenced by the season anymore.\n", + "\n", + "In order to describe this new interventional distribution we need truncated factorization:\n", + "\n", + "```{card} Truncated factorization\n", + "{cite:t}`pearl2000causality` describes truncated factorization as follows. If we have a probability distribution $P(v)$ on a set of $V$ variables, then $P_x(v)$ is the interventional distribution that results from $\\operatorname{do}(X=x)$ that sets a subset of $X$ variables to constants $x$. Then we can describe the interventional distribution with truncated factorization as:\n", + "\n", + "$$\n", + "P_x(v) = \\prod_{ \\{ i | V_i \\notin X \\} } P(v_i|pa_i)\n", + "$$\n", + "\n", + "This is actually quite simple. It can be thought of as exactly the same as the regular factorization of the joint distribution, but we are only including terms which do _not_ influence any intervened upon variable.\n", + "\n", + "Interested readers are referred to section 1.3 of {cite:t}`pearl2000causality` on Causal Bayesian Networks.\n", + "```\n", + "\n", + "Applying that to the spinkler example, we can define the _interventional distribution_ as:\n", + "\n", + "$$\n", + "P(x_1, x_2, \\operatorname{do}(x_3=1), x_4, x_5) = P(x_1) P(x_2|x_1) P(x_4|x_3=1, x_2) P(x_5|x_4)\n", + "$$\n", + "\n", + "There are two important changes here:\n", + "1. Note that $x_3$ was previously a random variable, but this has now been 'locked' at a particular value, $x_3=1$, because of our intervention.\n", + "2. Note the absense of the $P(x_3|x_1)$ term, because $x_1$ no longer has any causal influence over $x_3$.\n", + "\n", + "So in summary, this is pretty cool. We can use the $\\operatorname{do}$ operator to make in intervention in our model of the world. We can then observe the consequences of this intervention and make much better predictions of what will happen when we are active and intervene (actually or hypothetically) in the world. The accuracy is of course subject to how well our causal DAG reflects the real processes in the world.\n", + "\n", + "For those wanting further background information on the $\\operatorname{do}$ operator, explained from a different angle, readers should check out the richly diagrammed and well-explained blog post [Causal Effects via the Do-operator](https://towardsdatascience.com/causal-effects-via-the-do-operator-5415aefc834a) {cite:p}`Talebi2022dooperator`, the popular science book by {cite:t}`pearl2018why`, or the textbook by {cite:t}`molak2023ciadip`." + ] + }, + { + "cell_type": "markdown", + "id": "b70e42b0-61a1-4c62-8dd9-d973d60d7946", + "metadata": { + "editable": true, + "raw_mimetype": "", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Three different causal DAGs\n", + "\n", + ":::{note}\n", + "This section takes heavy inspiration from the post [Causal Inference 2: Illustrating Interventions via a Toy Example](https://www.inference.vc/causal-inference-2-illustrating-interventions-in-a-toy-example/) {cite:p}`Huszár2019causal2`. Imitation is the sincerest form of flattery.\n", + ":::\n", + "\n", + "If we think about how 2 variables, $x$ and $y$, are related we can come up with many different causal DAGs. Below we consider just 3 possibilities, which we'll label DAG 1, 2, and 3.\n", + "\n", + "1. $x$ causally influences $y$\n", + "2. $y$ causally influences $x$\n", + "3. $z$ causally influences both $x$ and $y$\n", + "\n", + "We can draw these more graphically below:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "73ed8c06-6dbb-4706-9120-7623d168cdcb", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "x1\n", + "\n", + "x\n", + "\n", + "\n", + "\n", + "y1\n", + "\n", + "y\n", + "\n", + "\n", + "\n", + "x1->y1\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "y2\n", + "\n", + "y\n", + "\n", + "\n", + "\n", + "x2\n", + "\n", + "x\n", + "\n", + "\n", + "\n", + "y2->x2\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "z\n", + "\n", + "z\n", + "\n", + "\n", + "\n", + "x\n", + "\n", + "x\n", + "\n", + "\n", + "\n", + "z->x\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "y\n", + "\n", + "y\n", + "\n", + "\n", + "\n", + "z->y\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "g = gr.Digraph()\n", + "\n", + "# DAG 1\n", + "g.node(name=\"x1\", label=\"x\")\n", + "g.node(name=\"y1\", label=\"y\")\n", + "g.edge(tail_name=\"x1\", head_name=\"y1\")\n", + "\n", + "# DAG 2\n", + "g.node(name=\"y2\", label=\"y\")\n", + "g.node(name=\"x2\", label=\"x\")\n", + "g.edge(tail_name=\"y2\", head_name=\"x2\")\n", + "\n", + "# DAG 3\n", + "g.node(name=\"z\", label=\"z\")\n", + "g.node(name=\"x\", label=\"x\")\n", + "g.node(name=\"y\", label=\"y\")\n", + "g.edge(tail_name=\"z\", head_name=\"x\")\n", + "g.edge(tail_name=\"z\", head_name=\"y\")\n", + "\n", + "g" + ] + }, + { + "cell_type": "markdown", + "id": "3bb5134c-f476-4b8f-9338-1ed07e1fbb13", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "We can also imagine implementing such causal DAGs in Python code to generate `N` random numbers. Each of these will give rise to specific joint distributions, $P(x, y)$, and in fact, because Ferenc Huszár was clever in his blog post, we'll see later that these will all give rise to the same joint distributions.\n", + "\n", + "**DAG 1**\n", + "\n", + "```{code-block} python\n", + "x = rng.normal(loc=0, scale=1, size=N)\n", + "y = x + 1 + np.sqrt(3) * rng.normal(size=N)\n", + "```\n", + "\n", + "**DAG 2**\n", + "\n", + "```{code-block} python\n", + "y = 1 + 2 * rng.normal(size=N)\n", + "x = (y - 1) / 4 + np.sqrt(3) * rng.normal(size=N) / 2\n", + "```\n", + "\n", + "**DAG 3**\n", + "\n", + "```{code-block} python\n", + "z = rng.normal(size=N)\n", + "y = z + 1 + np.sqrt(3) * rng.normal(size=N)\n", + "x = z\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "e2d1a7ca-1948-4165-92aa-3713df60b73b", + "metadata": {}, + "source": [ + ":::{note}\n", + "These code snippets are important because they define identical joint distributions $P(x,y)$ but they have different DAG structures. Therefore they may respond differently when it comes to making an intervention with the $\\operatorname{do}$ operator. It is worth referring back to these code snippets to make sure you understand how they relate to the DAG structures above and to think through how making interventions on variables will affect the values of each of the variables $x, y, z$ if at all.\n", + ":::" + ] + }, + { + "cell_type": "markdown", + "id": "453edf32-48d2-4d7f-96fb-6fb61851de9e", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "However, we are going to implement these using Bayesian causal DAGs with PyMC. Let's see how we can do this, then generate samples from them using `pm.sample_prior_predictive`. As we go with each DAG, we'll package the data up in `DataFrame`'s for plotting later, and also plot the graphviz representation of the PyMC models. You'll see that while these are a fraction more visually complex, they do actually match up with the causal DAGs we've specified above." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "cc4469dd-e40b-4f59-b6c5-3077043e9878", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# number of samples to generate\n", + "N = 1_000_000" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "380c7726-1168-4a47-8392-f7e5c3475f3f", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling: [temp, x]\n" + ] + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "x\n", + "\n", + "x\n", + "~\n", + "Normal\n", + "\n", + "\n", + "\n", + "y\n", + "\n", + "y\n", + "~\n", + "Deterministic\n", + "\n", + "\n", + "\n", + "x->y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "temp\n", + "\n", + "temp\n", + "~\n", + "Normal\n", + "\n", + "\n", + "\n", + "temp->y\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "with pm.Model() as model1:\n", + " x = pm.Normal(\"x\")\n", + " temp = pm.Normal(\"temp\")\n", + " y = pm.Deterministic(\"y\", x + 1 + np.sqrt(3) * temp)\n", + " idata1 = pm.sample_prior_predictive(samples=N, random_seed=rng)\n", + "\n", + "ds1 = az.extract(idata1.prior, var_names=[\"x\", \"y\"])\n", + "\n", + "pm.model_to_graphviz(model1)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "09e899ac-77b5-4efa-81b9-0c31a52843d9", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling: [temp, y]\n" + ] + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "x\n", + "\n", + "x\n", + "~\n", + "Deterministic\n", + "\n", + "\n", + "\n", + "temp\n", + "\n", + "temp\n", + "~\n", + "Normal\n", + "\n", + "\n", + "\n", + "temp->x\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "y\n", + "\n", + "y\n", + "~\n", + "Normal\n", + "\n", + "\n", + "\n", + "y->x\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "with pm.Model() as model2:\n", + " y = pm.Normal(\"y\", mu=1, sigma=2)\n", + " temp = pm.Normal(\"temp\")\n", + " x = pm.Deterministic(\"x\", (y - 1) / 4 + np.sqrt(3) * temp / 2)\n", + " idata2 = pm.sample_prior_predictive(samples=N, random_seed=rng)\n", + "\n", + "ds2 = az.extract(idata2.prior, var_names=[\"x\", \"y\"])\n", + "\n", + "pm.model_to_graphviz(model2)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "695ac444-5db0-4214-8afe-720ae3b4260e", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling: [temp, z]\n" + ] + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "x\n", + "\n", + "x\n", + "~\n", + "Deterministic\n", + "\n", + "\n", + "\n", + "temp\n", + "\n", + "temp\n", + "~\n", + "Normal\n", + "\n", + "\n", + "\n", + "y\n", + "\n", + "y\n", + "~\n", + "Deterministic\n", + "\n", + "\n", + "\n", + "temp->y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "z\n", + "\n", + "z\n", + "~\n", + "Normal\n", + "\n", + "\n", + "\n", + "z->x\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "z->y\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "with pm.Model() as model3:\n", + " z = pm.Normal(\"z\")\n", + " temp = pm.Normal(\"temp\")\n", + " y = pm.Deterministic(\"y\", z + 1 + np.sqrt(3) * temp)\n", + " x = pm.Deterministic(\"x\", z)\n", + " idata3 = pm.sample_prior_predictive(samples=N)\n", + "\n", + "ds3 = az.extract(idata3.prior, var_names=[\"x\", \"y\"])\n", + "\n", + "pm.model_to_graphviz(model3)" + ] + }, + { + "cell_type": "markdown", + "id": "07b66542-dd10-43b1-9dfb-5b438f756736", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Joint distributions, $P(x,y)$\n", + "\n", + "First, let's take a look at the joint distributions for each of the DAGs to convince ourselves that these are actually the same." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "49d3a21d-9ec3-4fd8-88c9-093aeae336ab", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 708, + "width": 1211 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 3, figsize=(12, 8), sharex=True, sharey=True)\n", + "\n", + "for i, ds in enumerate([ds1, ds2, ds3]):\n", + " az.plot_kde(\n", + " ds[\"x\"],\n", + " ds[\"y\"],\n", + " hdi_probs=[0.25, 0.5, 0.75, 0.9, 0.95],\n", + " contour_kwargs={\"colors\": None},\n", + " contourf_kwargs={\"alpha\": 0.5},\n", + " ax=ax[i],\n", + " )\n", + " ax[i].set(\n", + " title=f\"$P(x, y)$, DAG {i+1}\",\n", + " xlim=[-4, 4],\n", + " xticks=np.arange(-4, 4 + 1, step=2),\n", + " ylim=[-6, 8],\n", + " yticks=np.arange(-6, 8 + 1, step=2),\n", + " aspect=\"equal\",\n", + " )\n", + " ax[i].axvline(x=2, ls=\"--\", c=\"k\")" + ] + }, + { + "cell_type": "markdown", + "id": "6625a68d-e19e-4490-b486-29e3bff42716", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "At this point we have met 3 different data generating processes (and their corresponding DAGs). We've drawn many MCMC samples from the prior distribution and visualised this joint distribution $P(x,y)$ for each of the models. We are now in position to recap the conditional distributions (e.g. $P(y|x=2$, see the next section) and how they compare to the interventional distribution $P(y|\\operatorname{do}=2)$ in the section following that." + ] + }, + { + "cell_type": "markdown", + "id": "b44eef67-7ad3-46cc-b18c-e589b9fd271b", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Conditional distributions, $P(y|x=2)$" + ] + }, + { + "cell_type": "markdown", + "id": "2d30eb22-2560-495a-8af2-f9c9fe04848a", + "metadata": {}, + "source": [ + "In the MCMC spirit of representing probability distributions by samples, let's now calculate the conditional distributions. If we picked all the values where $x$ was _exactly_ 2, then we might not end up with any samples at all, so what we'll do is to take a very narrow slice of samples around 2. So these will be approximations - as the number of samples increases and the width of the slice decreases, then our approximation would become more accurate." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "4340f80a-95f5-486d-b958-88ec130b6538", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# Extract samples from P(y|x≈2)\n", + "conditional1 = ds1.query(sample=\"1.99 < x < 2.01\")[\"y\"]\n", + "conditional2 = ds2.query(sample=\"1.99 < x < 2.01\")[\"y\"]\n", + "conditional3 = ds3.query(sample=\"1.99 < x < 2.01\")[\"y\"]" + ] + }, + { + "cell_type": "markdown", + "id": "99ccc753-e3ef-4834-b02f-4a8d82749fe5", + "metadata": {}, + "source": [ + "So now we've got our MCMC estimates of $P(y|x=2)$ for all of the DAGs. But you're going to have to wait just a moment before we plot them. Let's move on to calculate $P(y|\\operatorname{do}(x=2))$ and then plot them in one go so we can compare." + ] + }, + { + "cell_type": "markdown", + "id": "d0896e42-ee27-4c37-a065-7c78d6f6c0e2", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Interventional distributions, $P(y|\\operatorname{do}(x=2))$\n", + "\n", + "In turn for each of the 3 DAGs, let's use the $\\operatorname{do}$ operator, setting $x=2$. This will give us a new DAG and we'll plot the graphviz representation and then take samples to represent the interventional distribution." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "87ef7495-d1db-4734-9685-e0963ed346b3", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "x\n", + "\n", + "x\n", + "~\n", + "ConstantData\n", + "\n", + "\n", + "\n", + "y\n", + "\n", + "y\n", + "~\n", + "Deterministic\n", + "\n", + "\n", + "\n", + "x->y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "temp\n", + "\n", + "temp\n", + "~\n", + "Normal\n", + "\n", + "\n", + "\n", + "temp->y\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model1_do = do(model1, {\"x\": 2})\n", + "pm.model_to_graphviz(model1_do)" + ] + }, + { + "cell_type": "markdown", + "id": "95844edc-2e47-47a1-986a-c6e0b30386c0", + "metadata": {}, + "source": [ + ":::{important}\n", + "Let's just take a moment to reflect on what we've done here! We took a model (`model1`) and then used the $\\operatorname{do}$ function and specified an intervention we wanted to make. In this case it was to set $x=2$. We then got back a new model where the original DAG has been mutated in the way that we set out above. Namely, we defined $x=2$ _and_ removed edges from incoming nodes to $x$. In this first DAG, there were no incoming edges, but this is the case in DAG2 and DAG 3 below.\n", + ":::" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "e7a60ccb-8568-42b2-9b59-75c1f812aecf", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "x\n", + "\n", + "x\n", + "~\n", + "ConstantData\n", + "\n", + "\n", + "\n", + "temp\n", + "\n", + "temp\n", + "~\n", + "Normal\n", + "\n", + "\n", + "\n", + "y\n", + "\n", + "y\n", + "~\n", + "Normal\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model2_do = do(model2, {\"x\": 2})\n", + "pm.model_to_graphviz(model2_do)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "ae53d8bf-a706-46e9-bd1c-2d43a5bcd222", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "x\n", + "\n", + "x\n", + "~\n", + "ConstantData\n", + "\n", + "\n", + "\n", + "temp\n", + "\n", + "temp\n", + "~\n", + "Normal\n", + "\n", + "\n", + "\n", + "y\n", + "\n", + "y\n", + "~\n", + "Deterministic\n", + "\n", + "\n", + "\n", + "temp->y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "z\n", + "\n", + "z\n", + "~\n", + "Normal\n", + "\n", + "\n", + "\n", + "z->y\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model3_do = do(model3, {\"x\": 2})\n", + "pm.model_to_graphviz(model3_do)" + ] + }, + { + "cell_type": "markdown", + "id": "688de710-654d-4905-a328-81941aaa1fe6", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "So we can see that in DAG 1, the $x$ variable still has causal influence on $y$. However, in DAGs 2 and 3, $y$ is no longer causally influenced by $x$. So in DAGs 2 and 3, our intervention $\\operatorname{do}(x=2)$ have no influence on $y$." + ] + }, + { + "cell_type": "markdown", + "id": "01ba8784-cd8b-45e8-b9ec-1fd6d56b010a", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Next we'll sample from each of these interventional distributions. Note that we are using the mutilated models, `model1_do`, `model2_do`, and `model3_do`. " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "e54e1a6e-f954-4a17-b548-a8db72ab0013", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling: [temp]\n", + "Sampling: [temp, y]\n", + "Sampling: [temp, z]\n" + ] + } + ], + "source": [ + "with model1_do:\n", + " idata1_do = pm.sample_prior_predictive(samples=N, random_seed=rng)\n", + "\n", + "with model2_do:\n", + " idata2_do = pm.sample_prior_predictive(samples=N, random_seed=rng)\n", + "\n", + "with model3_do:\n", + " idata3_do = pm.sample_prior_predictive(samples=N, random_seed=rng)" + ] + }, + { + "cell_type": "markdown", + "id": "d9d4fe62-b694-4a90-9e7f-21344f69fcf6", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "So let's compare the conditional and interventional distributions for all 3 DAGs." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "72a72a7f-cd77-45eb-a000-9d78bd01498e", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 411, + "width": 1011 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 2, figsize=(10, 4), sharex=True, sharey=True)\n", + "\n", + "az.plot_density(\n", + " [conditional1, conditional2, conditional3],\n", + " data_labels=[\"DAG 1\", \"DAG 2\", \"DAG 3\"],\n", + " combine_dims={\"sample\"},\n", + " ax=ax[0],\n", + " hdi_prob=1.0,\n", + ")\n", + "ax[0].set(xlabel=\"y\", title=\"Conditional distributions\\n$P(y|x=2)$\")\n", + "\n", + "az.plot_density(\n", + " [idata1_do, idata2_do, idata3_do],\n", + " data_labels=[\"DAG 1\", \"DAG 2\", \"DAG 3\"],\n", + " group=\"prior\",\n", + " var_names=\"y\",\n", + " ax=ax[1],\n", + " hdi_prob=1.0,\n", + ")\n", + "ax[1].set(xlabel=\"y\", title=\"Interventional distributions\\n$P(y|\\\\operatorname{do}(x=2))$\");" + ] + }, + { + "cell_type": "markdown", + "id": "f546f9e2-0f9b-4eaa-93e3-b5887b757a37", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "We can see, as expected, that the conditional distributions are the same for all 3 DAGs. Note that these distributions are not posterior distributions of estimated parameters - we have not conducted any parameter estimation here.\n", + "\n", + "The story is different for the interventional distributions however. Here, DAG 1 differs because it is the only one where our $\\operatorname{do}(x=2)$ intervention causally effects $y$. If we think about it further, because the $\\operatorname{do}$ has not affected the structure _for this DAG_, in this example $P(y|\\operatorname{do}(x=2)) = P(y|x=2)$. However this is _not_ something to be generalised, it is just something specific to this particular simple DAG. \n", + "\n", + "The intervention severed any causal influence of $x$ on $y$ in DAGs 2 and 3. Let's just recap what the mutated DAGs look like; the mutated DAG 2 is shown below, and we can see that $P(y|\\operatorname{do}(x=2)) = P(y)$." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "1b8c87ad-c3c3-4de5-8b9f-9a47f34f536f", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "y2\n", + "\n", + "y\n", + "\n", + "\n", + "\n", + "x2\n", + "\n", + "x\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "g = gr.Digraph()\n", + "g.node(name=\"y2\", label=\"y\")\n", + "g.node(name=\"x2\", label=\"x\")\n", + "g" + ] + }, + { + "cell_type": "markdown", + "id": "9c4122ee-9709-4c9c-b0eb-a4c20e1b7f3a", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "The mutated DAG 3 is shown below. We can see that for this DAG, $P(y|\\operatorname{do}(x=2)) = P(y|z)$. " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "115b2f76-383d-4aed-8249-53bb4f57b213", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "z\n", + "\n", + "z\n", + "\n", + "\n", + "\n", + "y\n", + "\n", + "y\n", + "\n", + "\n", + "\n", + "z->y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "x\n", + "\n", + "x\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "g = gr.Digraph()\n", + "g.node(name=\"z\", label=\"z\")\n", + "g.node(name=\"x\", label=\"x\")\n", + "g.node(name=\"y\", label=\"y\")\n", + "g.edge(tail_name=\"z\", head_name=\"y\")\n", + "g" + ] + }, + { + "cell_type": "markdown", + "id": "dec5c8d6-e562-47ca-a263-2e8269704d04", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "$P(y|\\operatorname{do}(x=2))$ for DAG 2 and DAG 3 will actually be the same in this contrived example because the details were arranged to arrive at the same marginal distribution $P(y)$ for all DAGs." + ] + }, + { + "cell_type": "markdown", + "id": "e546b0ab-dd66-4c20-b814-c8cd1bc6c710", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Summary\n", + "\n", + "Hopefuly, I've established a strong case for why we need to expand our skillset beyond the realm of Bayesian statistics alone. While these approaches are, and will always be, at the core of PyMC, the ecosystem is embracing causal reasoning.\n", + "\n", + "In particular, we've seen how we can use the new $\\operatorname{do}$ operator to implement realised or hypothetical interventions on causal models of the world to obtain interventional distributions. Understanding the underlying causal DAG and how interventions change this DAG are crucial components in building our understanding of causal reasoning.\n", + "\n", + "The exciting thing is that there are many more causal reasoning ideas and concepts to learn. And PyMC is adapting as needed to support all your Bayesian causal inference needs.\n", + "\n", + "Readers looking to learn more are suggested to check out the cited blog posts as well as textbooks, {cite:t}`pearl2000causality`, {cite:t}`pearl2016causal`, {cite:t}`mcelreath2018statistical`, {cite:t}`molak2023ciadip`." + ] + }, + { + "cell_type": "markdown", + "id": "f17a9b3b-a3c2-4919-893b-569049db03d6", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Authors\n", + "- Authored by [Benjamin T. Vincent](https://github.com/drbenvincent) in July 2023" + ] + }, + { + "cell_type": "markdown", + "id": "ecbb878d-531b-4c96-afe2-c39f928b9162", + "metadata": {}, + "source": [ + "## References\n", + "\n", + ":::{bibliography}\n", + ":filter: docname in docnames\n", + ":::" + ] + }, + { + "cell_type": "markdown", + "id": "9fd548d0-5977-4a19-935a-506e86063887", + "metadata": {}, + "source": [ + "## Watermark" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "265cd4a3-4e02-408c-afe6-80627f0663c4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Last updated: Tue Jul 04 2023\n", + "\n", + "Python implementation: CPython\n", + "Python version : 3.11.4\n", + "IPython version : 8.14.0\n", + "\n", + "pytensor: 2.12.3\n", + "xarray : 2023.6.0\n", + "\n", + "seaborn : 0.12.2\n", + "pymc_experimental: 0.0.7\n", + "arviz : 0.15.1\n", + "graphviz : 0.20.1\n", + "daft : 0.1.2\n", + "numpy : 1.25.0\n", + "packaging : 23.1\n", + "matplotlib : 3.7.1\n", + "pymc : 5.5.0\n", + "\n", + "Watermark: 2.3.1\n", + "\n" + ] + } + ], + "source": [ + "%load_ext watermark\n", + "%watermark -n -u -v -iv -w -p pytensor,xarray" + ] + }, + { + "cell_type": "markdown", + "id": "62e8040f-452c-4a11-90f5-fa94eb03c971", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + ":::{include} ../page_footer.md :::" + ] + } + ], + "metadata": { + "jupytext": { + "notebook_metadata_filter": "substitutions" + }, + "kernelspec": { + "display_name": "pymc_env", + "language": "python", + "name": "pymc_env" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + }, + "myst": { + "substitutions": { + "extra_dependencies": "daft pymc_experimental" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/causal_inference/interventional_distribution.myst.md b/examples/causal_inference/interventional_distribution.myst.md new file mode 100644 index 000000000..37bdf5a66 --- /dev/null +++ b/examples/causal_inference/interventional_distribution.myst.md @@ -0,0 +1,631 @@ +--- +jupytext: + notebook_metadata_filter: substitutions + text_representation: + extension: .md + format_name: myst + format_version: 0.13 +kernelspec: + display_name: pymc_env + language: python + name: pymc_env +--- + ++++ {"editable": true, "slideshow": {"slide_type": ""}, "tags": []} + +(interventional_distribution)= +# Interventional distributions and graph mutilation with the do-operator + +:::{post} July, 2023 +:tags: causal inference, do-operator, graph mutation +:category: beginner, explanation +:author: Benjamin T. Vincent +::: + ++++ {"editable": true, "slideshow": {"slide_type": ""}, "tags": []} + +[PyMC](https://github.com/pymc-devs/pymc) is a pivotal component of the open source Bayesian statistics ecosystem. It helps solve real problems across a wide range of industries and academic research areas every day. And it has gained this level of utility by being accessible, powerful, and practically useful at solving _Bayesian statistical inference_ problems. + +But times are changing. There's a [causal revolution](https://en.wikipedia.org/wiki/The_Book_of_Why) underway and there's a growing recognition that to answer some of the most interesting and challenging questions requires us to intergrate causal reasoning into our efforts. + +PyMC is rising to this challenge! While there are many novel causal concepts to learn, Bayesians will find that they are not starting from scratch. They are already pretty familiar with [Directed Acyclic Graphs (DAGs)](https://en.wikipedia.org/wiki/Directed_acyclic_graph) and so this gives a good jumping off point to gain relatively easy access into the world of **Bayesian causal inference**. + +This notebook is going to cover one of the foundational components of causal reasoning which has newly arrived to the PyMC ecosystem, the $\operatorname{do}$ operator. Indeed, depending on whose definitions you want to use, adding the $\operatorname{do}$ operator into the kind of Bayesian DAGs that PyMC users build every day gets us to the status of building [causal Bayesian networks](https://en.wikipedia.org/wiki/Causal_graph). + +If that sounds cool, let's dive in... + ++++ + +## Set up the notebook + +```{code-cell} ipython3 +--- +editable: true +slideshow: + slide_type: '' +tags: [] +--- +import arviz as az +import graphviz as gr +import matplotlib.pyplot as plt +import numpy as np +import pymc as pm +import seaborn as sns + +from packaging import version +``` + ++++ {"editable": true, "slideshow": {"slide_type": ""}, "tags": []} + +:::{include} ../extra_installs.md +::: + +This notebook relies on experimental functionality currently in the [pymc-experimental](https://github.com/pymc-devs/pymc-experimental) repository. In the near future this will be moved into the main [pymc](https://github.com/pymc-devs/pymc) repository. + +```{code-cell} ipython3 +--- +editable: true +slideshow: + slide_type: '' +tags: [] +--- +# Import additional libraries that are not dependencies of PyMC +import daft +import pymc_experimental as pmx + +# Check we have the necessary versions to get the new experimental functionality. +assert version.parse(pm.__version__) >= version.parse("5.5.0") +assert version.parse(pmx.__version__) >= version.parse("0.0.7") + +# import the new functionality +from pymc_experimental.model_transform.conditioning import do +``` + +```{code-cell} ipython3 +--- +editable: true +slideshow: + slide_type: '' +tags: [] +--- +RANDOM_SEED = 123 +rng = np.random.default_rng(RANDOM_SEED) +az.style.use("arviz-darkgrid") +%config InlineBackend.figure_format = 'retina' +``` + ++++ {"editable": true, "slideshow": {"slide_type": ""}, "tags": []} + +## What can we do with Bayesian inference? + +Whether we are building _descriptive_ models or those that try to model the underlying processes, Bayesians are very used to building white box (i.e. the opposite of [black box](https://en.wikipedia.org/wiki/Black_box)), interpretable, models of [data generating processes](https://en.wikipedia.org/wiki/Data_generating_process). While we construct PyMC models using code, behind the scenes this is represented as a DAG, which we can visualise with graphviz. Let's see how this works using the example in the docs: + +```{code-cell} ipython3 +--- +editable: true +slideshow: + slide_type: '' +tags: [] +--- +J = 8 +y = np.array([28, 8, -3, 7, -1, 1, 18, 12]) +sigma = np.array([15, 10, 16, 11, 9, 11, 10, 18]) + +with pm.Model() as schools: + eta = pm.Normal("eta", 0, 1, shape=J) + mu = pm.Normal("mu", 0, sigma=1e6) + tau = pm.HalfCauchy("tau", 25) + theta = mu + tau * eta + obs = pm.Normal("obs", theta, sigma=sigma, observed=y) + +pm.model_to_graphviz(schools) +``` + ++++ {"editable": true, "slideshow": {"slide_type": ""}, "tags": []} + +Regardless of the particular model or models we are working with, we can do a whole range of _statistical_ procedures: + +* We could examine the [prior predictive distribution](https://en.wikipedia.org/wiki/Posterior_predictive_distribution#Prior_vs._posterior_predictive_distribution) to see what we'd expect to see in the given DAG based on our stated prior beliefs with `pymc.sample_prior_predictive`. An example use case would be when we want to understand our predictions of how inflation may evolve into the future based on the structure of our model (e.g. the national and international economy) and our prior beliefs over latent variables. +* We could conduct Bayesian inference by sampling from the posterior distribution with `pymc.sample`. This would update our beliefs to assign credibility to different values of latent variables given the data that we have observed. For example, maybe we get another inflation data point added to our dataset and we want to update our beliefs about the latent variables in the model of the economy. +* We could examine the [posterior predictive distribution](https://en.wikipedia.org/wiki/Posterior_predictive_distribution) using `pymc.sample_posterior_predictive`. This is closely related to the prior predictive distribution, but in our running example it would allow us to create a revised set of predictions about future inflation rates after we've observed another data point. +* If we wanted, we could get fancy with {ref}`GLM-model-selection` to compare different models (data generating processes). This could be particularly useful because we arguably don't have complete faith that we know the "true" model of the economy, even at a coarse level of abstraction. So we could build multiple models (DAGs) and evaluate the relative credibility that each model generated the observed data. +* If we have a number of candidate data generating processes, we could incorporate our uncertainty in the data generating process through {ref}`model_averaging`. + +If we've mastered all of these steps, we can rightfully feel pretty happy with ourselves. We can accomplish a lot with these statistical and predictive procedures. + ++++ {"editable": true, "slideshow": {"slide_type": ""}, "tags": ["hide-input"]} + +## Why causality is important + +But now it's time to get smacked in our smug Bayesian face. As others have argued (e.g. {cite:t}`pearl2018why,pearl2000causality`), it is entirely possible to build a pretty good _predictive_ model, but one which can catestrophically fail the moment you (or anyone else) intervenes in the system. Such interventions can totally destroy predictive modelling approaches and wake you up real fast to the necessity of adding causal reasoning into our skillset. + +In our running example, this could correspond to when a central bank switches from making predictions about inflation to now _acting_ and _intervening_ in the system by, for example, changing interest rates. All of a sudden you might be faced with a situation where the economy does not respond to your intervention as you predicted. + ++++ {"editable": true, "slideshow": {"slide_type": ""}, "tags": []} + +Let's consider a seemingly trivial example with 3 nodes to see how we can get fooled. The image below shows two different causal DAGs. On the left we are interested in how $X$ causally affects $Y$, both directly and indirectly through a mediating variable $M$. If we take a purely statistical approach (e.g. {ref}`mediation_analysis`) we might find that the data is very plausibly generated by this DAG. This might give us the confidence to conduct an intervention on $M$ with the aim of influencing our target outcome, $Y$. But when we do this intervention in the real world and change $M$, we actually find absolutely no change in $Y$. What is going on here? + +```{code-cell} ipython3 +--- +editable: true +slideshow: + slide_type: '' +tags: [hide-input] +--- +g = gr.Digraph() +# Wrong data generating process +g.node(name="x2", label="X") +g.node(name="y2", label="Y") +g.node(name="m2", label="M") +g.edge(tail_name="x2", head_name="y2") +g.edge(tail_name="x2", head_name="m2") +g.edge(tail_name="m2", head_name="y2") +# Actual causal DAG +g.node(name="x", label="X") +g.node(name="y", label="Y") +g.node(name="m", label="M") +g.node(name="u", label="U", color="lightgrey", style="filled") +g.edge(tail_name="x", head_name="y") +g.edge(tail_name="x", head_name="m") +g.edge(tail_name="m", head_name="y", style="dashed", dir="none") +g.edge(tail_name="u", head_name="m", color="lightgrey") +g.edge(tail_name="u", head_name="y", color="lightgrey") +# Render +g +``` + ++++ {"editable": true, "slideshow": {"slide_type": ""}, "tags": []} + +Little did we know, but the _actual_ data generating process is captured by the DAG on the right. This shows that $X$ does causally influence both $M$ and $Y$, however $M$ does not in fact causally affect $Y$. Instead, there is an unobserved variable $U$ which causally influences both $M$ and $Y$. This unobserved confounder creates a backdoor path in which _statistical_ association may flow between the path $X \rightarrow M \rightarrow U \rightarrow Y$. All this causes a statistical association between $M$ and $Y$ which our purely statistical approach mislead us into thinking that $M$ did causally influence $Y$ when it did not. No wonder our intervention failed to have any effects. + +Our mistake was to interpret a statistical model causally. + ++++ {"editable": true, "slideshow": {"slide_type": ""}, "tags": []} + +## Statistical versus interventional distributions +So far this has been quite high-level, but let's try to pin this down a little. In our example, if we were to take a purely statistical approach we could ask "What happened when interest rates were 2%?" This is a statistical question because we are basically looking back in our dataset and filtering (or conditioning) upon time points where interest rates were at (or very close to) 2%. So let's flag up - **conditional distributions are purely statistical quantities**. + +Though the real question we might want an answer to is "What would have happened in the past if we had set the interest rates to 2%?" or "What will happen going forward if we set the interest rates to 2%?" Despite the subtle changing of wording, this now radically changes what we have to do in order to answer the question. So a key point here is **interventional distributions require causal (not statistical) approaches**. + +Interventional distributions are cool because they allow us to ask what-if (or counterfactual questions). For example, with a causal DAG we could ask questions of the form, "What do I think will happen in the future if I do X?" or "What do I think would have happened in the past if X had happened?" See how these types of questions have a very different flavour to purely statistical kinds of questions - they would be more like "Given what I've seen, what do I think will happen." See how this has a more passive, observational focus. + +From hereon, the main point of this notebook will be to provide some understanding and intuition about the differences between conditional and interventional distributions, and how to estimate interventional distributions with PyMC. As we said above, we can use the $\operatorname{do}$ operator to estimate interventional distributions. So let's dive in and see how that works. + ++++ {"editable": true, "slideshow": {"slide_type": ""}, "tags": []} + +## Interventions and the $\operatorname{do}$ operator + +We'll consider an example from {cite:t}`pearl2000causality` where we examine a DAG which is a putative causal explanation of how various factors influence each other to result in grass becoming slippery. The left shows our causal DAG, and the right shows how the DAG is changed if we consider an intervention (hypothetical or actual) where we turn the sprinkler on. The $\operatorname{do}$ operator implements an intervention that we want to make. It consists of 2 simple steps: + +1. It takes a given node in a graph and sets that node at the desired value. +2. It removes any causal influence on this node by other nodes. It does this by removing all incoming edges into that node. + +![](sprinkler.png) + +On the left of the figure we have a causal DAG describing the causal relationships between season, whether a sprinkler has been on, whether it has rained, if the grass is wet, and if the grass is slippery. + +The joint distribution can be factorised as: + +$$ +P(x_1, x_2, x_3, x_4, x_5) = P(x_1) P(x_3|x_1) P(x_2|x_1) P(x_4|x_3, x_2) P(x_5|x_4) +$$ + +```{card} Factorizing joint distributions +For a DAG, a complex joint distribution can be broken down into the product of conditional distributions: + +$$ +P(x_1, x_2, \ldots, x_n) = \prod_i P(x_i|pa_i) +$$ + +where $pa_i$ are the parents of node $x_i$, and $i = \{ 1, \ldots, n \}$. +``` + +On the right of the figure we have applied the $\operatorname{do}$ operator to examine what will happen if we set the sprinkler to be on. You can see that we have now set the value of that node, $x_3=1$ and we have removed the incoming edge (influence) of season, meaning that once we turn on the sprinkler manually, it's not influenced by the season anymore. + +In order to describe this new interventional distribution we need truncated factorization: + +```{card} Truncated factorization +{cite:t}`pearl2000causality` describes truncated factorization as follows. If we have a probability distribution $P(v)$ on a set of $V$ variables, then $P_x(v)$ is the interventional distribution that results from $\operatorname{do}(X=x)$ that sets a subset of $X$ variables to constants $x$. Then we can describe the interventional distribution with truncated factorization as: + +$$ +P_x(v) = \prod_{ \{ i | V_i \notin X \} } P(v_i|pa_i) +$$ + +This is actually quite simple. It can be thought of as exactly the same as the regular factorization of the joint distribution, but we are only including terms which do _not_ influence any intervened upon variable. + +Interested readers are referred to section 1.3 of {cite:t}`pearl2000causality` on Causal Bayesian Networks. +``` + +Applying that to the spinkler example, we can define the _interventional distribution_ as: + +$$ +P(x_1, x_2, \operatorname{do}(x_3=1), x_4, x_5) = P(x_1) P(x_2|x_1) P(x_4|x_3=1, x_2) P(x_5|x_4) +$$ + +There are two important changes here: +1. Note that $x_3$ was previously a random variable, but this has now been 'locked' at a particular value, $x_3=1$, because of our intervention. +2. Note the absense of the $P(x_3|x_1)$ term, because $x_1$ no longer has any causal influence over $x_3$. + +So in summary, this is pretty cool. We can use the $\operatorname{do}$ operator to make in intervention in our model of the world. We can then observe the consequences of this intervention and make much better predictions of what will happen when we are active and intervene (actually or hypothetically) in the world. The accuracy is of course subject to how well our causal DAG reflects the real processes in the world. + +For those wanting further background information on the $\operatorname{do}$ operator, explained from a different angle, readers should check out the richly diagrammed and well-explained blog post [Causal Effects via the Do-operator](https://towardsdatascience.com/causal-effects-via-the-do-operator-5415aefc834a) {cite:p}`Talebi2022dooperator`, the popular science book by {cite:t}`pearl2018why`, or the textbook by {cite:t}`molak2023ciadip`. + ++++ {"editable": true, "raw_mimetype": "", "slideshow": {"slide_type": ""}, "tags": []} + +## Three different causal DAGs + +:::{note} +This section takes heavy inspiration from the post [Causal Inference 2: Illustrating Interventions via a Toy Example](https://www.inference.vc/causal-inference-2-illustrating-interventions-in-a-toy-example/) {cite:p}`Huszár2019causal2`. Imitation is the sincerest form of flattery. +::: + +If we think about how 2 variables, $x$ and $y$, are related we can come up with many different causal DAGs. Below we consider just 3 possibilities, which we'll label DAG 1, 2, and 3. + +1. $x$ causally influences $y$ +2. $y$ causally influences $x$ +3. $z$ causally influences both $x$ and $y$ + +We can draw these more graphically below: + +```{code-cell} ipython3 +--- +editable: true +slideshow: + slide_type: '' +tags: [hide-input] +--- +g = gr.Digraph() + +# DAG 1 +g.node(name="x1", label="x") +g.node(name="y1", label="y") +g.edge(tail_name="x1", head_name="y1") + +# DAG 2 +g.node(name="y2", label="y") +g.node(name="x2", label="x") +g.edge(tail_name="y2", head_name="x2") + +# DAG 3 +g.node(name="z", label="z") +g.node(name="x", label="x") +g.node(name="y", label="y") +g.edge(tail_name="z", head_name="x") +g.edge(tail_name="z", head_name="y") + +g +``` + ++++ {"editable": true, "slideshow": {"slide_type": ""}, "tags": []} + +We can also imagine implementing such causal DAGs in Python code to generate `N` random numbers. Each of these will give rise to specific joint distributions, $P(x, y)$, and in fact, because Ferenc Huszár was clever in his blog post, we'll see later that these will all give rise to the same joint distributions. + +**DAG 1** + +```{code-block} python +x = rng.normal(loc=0, scale=1, size=N) +y = x + 1 + np.sqrt(3) * rng.normal(size=N) +``` + +**DAG 2** + +```{code-block} python +y = 1 + 2 * rng.normal(size=N) +x = (y - 1) / 4 + np.sqrt(3) * rng.normal(size=N) / 2 +``` + +**DAG 3** + +```{code-block} python +z = rng.normal(size=N) +y = z + 1 + np.sqrt(3) * rng.normal(size=N) +x = z +``` + ++++ + +:::{note} +These code snippets are important because they define identical joint distributions $P(x,y)$ but they have different DAG structures. Therefore they may respond differently when it comes to making an intervention with the $\operatorname{do}$ operator. It is worth referring back to these code snippets to make sure you understand how they relate to the DAG structures above and to think through how making interventions on variables will affect the values of each of the variables $x, y, z$ if at all. +::: + ++++ {"editable": true, "slideshow": {"slide_type": ""}, "tags": []} + +However, we are going to implement these using Bayesian causal DAGs with PyMC. Let's see how we can do this, then generate samples from them using `pm.sample_prior_predictive`. As we go with each DAG, we'll package the data up in `DataFrame`'s for plotting later, and also plot the graphviz representation of the PyMC models. You'll see that while these are a fraction more visually complex, they do actually match up with the causal DAGs we've specified above. + +```{code-cell} ipython3 +--- +editable: true +slideshow: + slide_type: '' +tags: [] +--- +# number of samples to generate +N = 1_000_000 +``` + +```{code-cell} ipython3 +--- +editable: true +slideshow: + slide_type: '' +tags: [] +--- +with pm.Model() as model1: + x = pm.Normal("x") + temp = pm.Normal("temp") + y = pm.Deterministic("y", x + 1 + np.sqrt(3) * temp) + idata1 = pm.sample_prior_predictive(samples=N, random_seed=rng) + +ds1 = az.extract(idata1.prior, var_names=["x", "y"]) + +pm.model_to_graphviz(model1) +``` + +```{code-cell} ipython3 +--- +editable: true +slideshow: + slide_type: '' +tags: [] +--- +with pm.Model() as model2: + y = pm.Normal("y", mu=1, sigma=2) + temp = pm.Normal("temp") + x = pm.Deterministic("x", (y - 1) / 4 + np.sqrt(3) * temp / 2) + idata2 = pm.sample_prior_predictive(samples=N, random_seed=rng) + +ds2 = az.extract(idata2.prior, var_names=["x", "y"]) + +pm.model_to_graphviz(model2) +``` + +```{code-cell} ipython3 +--- +editable: true +slideshow: + slide_type: '' +tags: [] +--- +with pm.Model() as model3: + z = pm.Normal("z") + temp = pm.Normal("temp") + y = pm.Deterministic("y", z + 1 + np.sqrt(3) * temp) + x = pm.Deterministic("x", z) + idata3 = pm.sample_prior_predictive(samples=N) + +ds3 = az.extract(idata3.prior, var_names=["x", "y"]) + +pm.model_to_graphviz(model3) +``` + ++++ {"editable": true, "slideshow": {"slide_type": ""}, "tags": []} + +### Joint distributions, $P(x,y)$ + +First, let's take a look at the joint distributions for each of the DAGs to convince ourselves that these are actually the same. + +```{code-cell} ipython3 +--- +editable: true +slideshow: + slide_type: '' +tags: [hide-input] +--- +fig, ax = plt.subplots(1, 3, figsize=(12, 8), sharex=True, sharey=True) + +for i, ds in enumerate([ds1, ds2, ds3]): + az.plot_kde( + ds["x"], + ds["y"], + hdi_probs=[0.25, 0.5, 0.75, 0.9, 0.95], + contour_kwargs={"colors": None}, + contourf_kwargs={"alpha": 0.5}, + ax=ax[i], + ) + ax[i].set( + title=f"$P(x, y)$, DAG {i+1}", + xlim=[-4, 4], + xticks=np.arange(-4, 4 + 1, step=2), + ylim=[-6, 8], + yticks=np.arange(-6, 8 + 1, step=2), + aspect="equal", + ) + ax[i].axvline(x=2, ls="--", c="k") +``` + ++++ {"editable": true, "slideshow": {"slide_type": ""}, "tags": []} + +At this point we have met 3 different data generating processes (and their corresponding DAGs). We've drawn many MCMC samples from the prior distribution and visualised this joint distribution $P(x,y)$ for each of the models. We are now in position to recap the conditional distributions (e.g. $P(y|x=2$, see the next section) and how they compare to the interventional distribution $P(y|\operatorname{do}=2)$ in the section following that. + ++++ {"editable": true, "slideshow": {"slide_type": ""}, "tags": []} + +### Conditional distributions, $P(y|x=2)$ + ++++ + +In the MCMC spirit of representing probability distributions by samples, let's now calculate the conditional distributions. If we picked all the values where $x$ was _exactly_ 2, then we might not end up with any samples at all, so what we'll do is to take a very narrow slice of samples around 2. So these will be approximations - as the number of samples increases and the width of the slice decreases, then our approximation would become more accurate. + +```{code-cell} ipython3 +--- +editable: true +slideshow: + slide_type: '' +tags: [] +--- +# Extract samples from P(y|x≈2) +conditional1 = ds1.query(sample="1.99 < x < 2.01")["y"] +conditional2 = ds2.query(sample="1.99 < x < 2.01")["y"] +conditional3 = ds3.query(sample="1.99 < x < 2.01")["y"] +``` + +So now we've got our MCMC estimates of $P(y|x=2)$ for all of the DAGs. But you're going to have to wait just a moment before we plot them. Let's move on to calculate $P(y|\operatorname{do}(x=2))$ and then plot them in one go so we can compare. + ++++ {"editable": true, "slideshow": {"slide_type": ""}, "tags": []} + +### Interventional distributions, $P(y|\operatorname{do}(x=2))$ + +In turn for each of the 3 DAGs, let's use the $\operatorname{do}$ operator, setting $x=2$. This will give us a new DAG and we'll plot the graphviz representation and then take samples to represent the interventional distribution. + +```{code-cell} ipython3 +--- +editable: true +slideshow: + slide_type: '' +tags: [] +--- +model1_do = do(model1, {"x": 2}) +pm.model_to_graphviz(model1_do) +``` + +:::{important} +Let's just take a moment to reflect on what we've done here! We took a model (`model1`) and then used the $\operatorname{do}$ function and specified an intervention we wanted to make. In this case it was to set $x=2$. We then got back a new model where the original DAG has been mutated in the way that we set out above. Namely, we defined $x=2$ _and_ removed edges from incoming nodes to $x$. In this first DAG, there were no incoming edges, but this is the case in DAG2 and DAG 3 below. +::: + +```{code-cell} ipython3 +model2_do = do(model2, {"x": 2}) +pm.model_to_graphviz(model2_do) +``` + +```{code-cell} ipython3 +model3_do = do(model3, {"x": 2}) +pm.model_to_graphviz(model3_do) +``` + ++++ {"editable": true, "slideshow": {"slide_type": ""}, "tags": []} + +So we can see that in DAG 1, the $x$ variable still has causal influence on $y$. However, in DAGs 2 and 3, $y$ is no longer causally influenced by $x$. So in DAGs 2 and 3, our intervention $\operatorname{do}(x=2)$ have no influence on $y$. + ++++ {"editable": true, "slideshow": {"slide_type": ""}, "tags": []} + +Next we'll sample from each of these interventional distributions. Note that we are using the mutilated models, `model1_do`, `model2_do`, and `model3_do`. + +```{code-cell} ipython3 +--- +editable: true +slideshow: + slide_type: '' +tags: [] +--- +with model1_do: + idata1_do = pm.sample_prior_predictive(samples=N, random_seed=rng) + +with model2_do: + idata2_do = pm.sample_prior_predictive(samples=N, random_seed=rng) + +with model3_do: + idata3_do = pm.sample_prior_predictive(samples=N, random_seed=rng) +``` + ++++ {"editable": true, "slideshow": {"slide_type": ""}, "tags": []} + +So let's compare the conditional and interventional distributions for all 3 DAGs. + +```{code-cell} ipython3 +--- +editable: true +slideshow: + slide_type: '' +tags: [hide-input] +--- +fig, ax = plt.subplots(1, 2, figsize=(10, 4), sharex=True, sharey=True) + +az.plot_density( + [conditional1, conditional2, conditional3], + data_labels=["DAG 1", "DAG 2", "DAG 3"], + combine_dims={"sample"}, + ax=ax[0], + hdi_prob=1.0, +) +ax[0].set(xlabel="y", title="Conditional distributions\n$P(y|x=2)$") + +az.plot_density( + [idata1_do, idata2_do, idata3_do], + data_labels=["DAG 1", "DAG 2", "DAG 3"], + group="prior", + var_names="y", + ax=ax[1], + hdi_prob=1.0, +) +ax[1].set(xlabel="y", title="Interventional distributions\n$P(y|\\operatorname{do}(x=2))$"); +``` + ++++ {"editable": true, "slideshow": {"slide_type": ""}, "tags": []} + +We can see, as expected, that the conditional distributions are the same for all 3 DAGs. Note that these distributions are not posterior distributions of estimated parameters - we have not conducted any parameter estimation here. + +The story is different for the interventional distributions however. Here, DAG 1 differs because it is the only one where our $\operatorname{do}(x=2)$ intervention causally effects $y$. If we think about it further, because the $\operatorname{do}$ has not affected the structure _for this DAG_, in this example $P(y|\operatorname{do}(x=2)) = P(y|x=2)$. However this is _not_ something to be generalised, it is just something specific to this particular simple DAG. + +The intervention severed any causal influence of $x$ on $y$ in DAGs 2 and 3. Let's just recap what the mutated DAGs look like; the mutated DAG 2 is shown below, and we can see that $P(y|\operatorname{do}(x=2)) = P(y)$. + +```{code-cell} ipython3 +--- +editable: true +slideshow: + slide_type: '' +tags: [hide-input] +--- +g = gr.Digraph() +g.node(name="y2", label="y") +g.node(name="x2", label="x") +g +``` + ++++ {"editable": true, "slideshow": {"slide_type": ""}, "tags": []} + +The mutated DAG 3 is shown below. We can see that for this DAG, $P(y|\operatorname{do}(x=2)) = P(y|z)$. + +```{code-cell} ipython3 +--- +editable: true +slideshow: + slide_type: '' +tags: [hide-input] +--- +g = gr.Digraph() +g.node(name="z", label="z") +g.node(name="x", label="x") +g.node(name="y", label="y") +g.edge(tail_name="z", head_name="y") +g +``` + ++++ {"editable": true, "slideshow": {"slide_type": ""}, "tags": []} + +$P(y|\operatorname{do}(x=2))$ for DAG 2 and DAG 3 will actually be the same in this contrived example because the details were arranged to arrive at the same marginal distribution $P(y)$ for all DAGs. + ++++ {"editable": true, "slideshow": {"slide_type": ""}, "tags": []} + +## Summary + +Hopefuly, I've established a strong case for why we need to expand our skillset beyond the realm of Bayesian statistics alone. While these approaches are, and will always be, at the core of PyMC, the ecosystem is embracing causal reasoning. + +In particular, we've seen how we can use the new $\operatorname{do}$ operator to implement realised or hypothetical interventions on causal models of the world to obtain interventional distributions. Understanding the underlying causal DAG and how interventions change this DAG are crucial components in building our understanding of causal reasoning. + +The exciting thing is that there are many more causal reasoning ideas and concepts to learn. And PyMC is adapting as needed to support all your Bayesian causal inference needs. + +Readers looking to learn more are suggested to check out the cited blog posts as well as textbooks, {cite:t}`pearl2000causality`, {cite:t}`pearl2016causal`, {cite:t}`mcelreath2018statistical`, {cite:t}`molak2023ciadip`. + ++++ {"editable": true, "slideshow": {"slide_type": ""}, "tags": []} + +## Authors +- Authored by [Benjamin T. Vincent](https://github.com/drbenvincent) in July 2023 + ++++ + +## References + +:::{bibliography} +:filter: docname in docnames +::: + ++++ + +## Watermark + +```{code-cell} ipython3 +%load_ext watermark +%watermark -n -u -v -iv -w -p pytensor,xarray +``` + ++++ {"editable": true, "slideshow": {"slide_type": ""}, "tags": []} + +:::{include} ../page_footer.md ::: diff --git a/examples/causal_inference/sprinkler.png b/examples/causal_inference/sprinkler.png new file mode 100644 index 000000000..b36893e40 Binary files /dev/null and b/examples/causal_inference/sprinkler.png differ diff --git a/examples/references.bib b/examples/references.bib index ff89ca9eb..41b63d4af 100644 --- a/examples/references.bib +++ b/examples/references.bib @@ -303,6 +303,13 @@ @book{huntington2021effect year = {2021}, publisher = {Chapman and Hall/CRC} } +@online{Huszár2019causal2, + author = {Husz\'{a}r, Ferenc}, + title = {Causal Inference 2: Illustrating Interventions via a Toy Example}, + year = {2019}, + url = {https://www.inference.vc/causal-inference-2-illustrating-interventions-in-a-toy-example/}, + urldate = {2023-07-01} +} @article{iacobucci2016mean, title = {Mean centering helps alleviate ``micro'' but not ``macro'' multicollinearity}, author = {Iacobucci, Dawn and Schneider, Matthew J and Popovich, Deidre L and Bakamitsos, Georgios A}, @@ -507,6 +514,12 @@ @misc{mnih2013playing archiveprefix = {arXiv}, primaryclass = {cs.LG} } +@book{molak2023ciadip, + title = {Causal Inference and Discovery in Python}, + author = {Molak, Aleksander}, + year = {2023}, + publisher = {Packt Publishing} +} @book{molnar2019, title = {Interpretable Machine Learning}, author = {Christoph Molnar}, @@ -544,6 +557,25 @@ @unpublished{padonou2015polar month = Feb, pdf = {https://hal.archives-ouvertes.fr/hal-01119942v1/file/PolarGP\_CircularDomains.pdf} } +@book{pearl2000causality, + title = {Causality: Models, reasoning and inference}, + author = {Pearl, Judea}, + publisher = {Cambridge University Press}, + year = {2000}, + isbn = {978-0521895606} +} +@book{pearl2016causal, + title = {Causal inference in statistics: A primer}, + author = {Glymour, Madelyn and Pearl, Judea and Jewell, Nicholas P}, + year = {2016}, + publisher = {John Wiley \& Sons} +} +@book{pearl2018why, + title = {The book of why: the new science of cause and effect}, + author = {Pearl, Judea and Mackenzie, Dana}, + year = {2018}, + publisher = {Basic books} +} @misc{quiroga2022bart, title = {Bayesian additive regression trees for probabilistic programming}, author = {Quiroga, Miriana and Garay, Pablo G and Alonso, Juan M. and Loyola, Juan Martin and Martin, Osvaldo A}, @@ -626,6 +658,13 @@ @misc{szegedy2014going archiveprefix = {arXiv}, primaryclass = {cs.CV} } +@online{Talebi2022dooperator, + author = {Talebi, Shawhin}, + title = {Causal Effects via the Do-operator}, + year = {2022}, + url = {https://towardsdatascience.com/causal-effects-via-the-do-operator-5415aefc834a}, + urldate = {2023-07-01} +} @article{taylor2018forecasting, title = {Forecasting at scale}, author = {Taylor, Sean J and Letham, Benjamin},