Causal Inference Wiki

A friendly introduction to causal inference

DISCLAIMER: this is an alpha-version of this project. We hope to make it grow in quality and quantity as more people get involved. If interested, please reach out!

This is meant to be a resource for causal-curious folks who are looking for accessible introduction to common topics in causal inference.

• We do not aim at replacing Wikipedia, or any of the excellent textbooks already out there on the topic.

• We do aim to include quick references to common topics of interest, and grow naturally with community's needs, expertise and interests. We hope to make this a collaborative project, so please get in touch if you'd like to make a contribution, or jump on it and create an issue or pull request.

Table of Contents

1. Common Terms (Terminology)

   1. G-something terms (Overview)
      1. G-estimation
      2. G-formula
      3. G-Identifiably
      4. G-etc.
   2. Interventions
   3. Counterfactuals

2. Causal Discovery

3. Common Assumptions

   1. Positivity
   2. SUTVA
   3. Consistency
   4. Compliance
   5. Exchangeability
      1. Ignorability (weak and strong)
      2. No-unmeasured confounding

4. Types of Causal Effects

   1. ATE
   2. CATE
   4. ITE
   5. Bounds on causal effects

5. Potential Outcomes vs. Graphical Models

   1. Other types of causality
      1. Granger causality, Causal Impact, etc.

6. (Causal Effects) Identifiability

   1. Challenges to identifiability: sources of bias
      1. Confounding
      2. (Sample) Selection Bias(Common_terms/Identifiability/Bias/Selection_bias.md)
   2. Common methods for identification
      1. Instrumental variables (IVs)
      2. Diffs in Diffs
      3. Doubly robust methods
         1. 2 step regression/IV regression
      4. Meta-learners
         1. S-learner
         2. T-learner
         3. R-learner
         4. X-learner
      5. Negative controls
      6. Method of Moments (moment matching?)
      7. Propensity score and matching
      7. Do-calculus
         1. Backdoor criterion + adjustment
         2. Frontdoor criterion + adjustment
         3. d-separation
      8. Proxy variables

7. Counterfactuals

8. Philosophy of Causality

   1. Nancy Cartwright
      1. Hunting Causes
      2. Refer to Stanford Encyclopedia
   2. Inspiration
      1. http://marcfbellemare.com/wordpress/metrics-mondays
         1. e.g. http://marcfbellemare.com/wordpress/12869
      2. http://causality.cs.ucla.edu/blog/index.php/2014/11/09/causal-inference-without-graphs/

Community and Recommendations

1. [Suggested readings]

   1. Textbooks
   2. Twitter/Blogs

2. Software

   1. Packages etc.
      1. awesome-causalinference
      2. Uber's Causal ML

How to contribute

A general format for an entry is encouraged to have the following structure:

1. Motivation
2. Definition
3. Intuition (including examples and relation to other concepts)
4. Further reading

We aim for entries to provide a complete and concise introduction, with pointers to more elaborate sources.

Credit

The Causal Inference Handbook is a joint effort by these contributors