Description

In the era of big data, the increasing availability of huge data sets can paradoxically be harmful when our causal inference method is designed to search for a causal model that is faithful to our data. Under the commonly made Causal Faithfulness Assumption, we look for patterns of dependencies and independencies in the data and match them with causal models that imply the same patterns. However, given enough data, we start picking up on the fact that everything is ultimately connected. These interactions are not normally picked up in small samples. The only faithful causal model in the limit of a large number of samples (the large-sample limit) therefore becomes the one where everything is connected. Alas, we cannot extract any useful causal information from a completely connected structure without making additional (strong) assumptions. We propose an alternative approach (RoCELL) that replaces the Causal Faithfulness Assumption with a prior that reflects the existence of many "weak" (irrelevant) and "strong" interactions. RoCELL outputs a posterior distribution over the target causal effect estimator that leads to good estimates even in the large-sample limit.

Content

The data set contains source code implementing the RoCELL algorithm, which is described in the article titled "Robust Causal Estimation in the Large-Sample Limit without Strict Faithfulness" by Ioan Gabriel Bucur, Tom Claassen and Tom Heskes. The data set also contains simulated data necessary for exactly reproducing the figures in the article as well as the routines necessary for recreating it. This research is presented in Chapter 2 of the PhD thesis titled "Being Bayesian about Causal Inference" by Ioan Gabriel Bucur.

The code is written in the R and C++ programming languages. RoCELL makes use of the MultiNest nested sampling algorithm, which is owned and copyrighted by Farhan Feroz and Mike Hobson. The MultiNest source code is bundled (as-is, except for build and install configuration) as a submodule in this package. For more details, please see the README accompanying the MultiNest submodule.

Structure

The code is structured on the skeleton of an R package package as follows:

• The folder data contains pre-saved simulated data, which we use to recreate the figures from the article. The simulated data can also be reproduced using the aistats-article-figures.R script in the main folder. The simulated data sets are described in R/data.R.

• The folder MultiNest contains the MultiNest submodule, which is a fork of the project developed by Farhan Feroz and Mike Hobson. The MultiNest submodule implements a complex nested sampling algorithm, which we employ in RoCELL for estimating model evidences and producing posterior samples. The original implementation is kept unchanged, with the exception of changes in the build configuration file made for easier compilation and installation.

• The folder R contains the R files necessary for reproducing the figures from the article.

• The folder man contains the documentation for the implemented functions.

• The folder posterior contains the C++ implementation of RoCELL written for integration with MultiNest.

• The folder src contains an Rcpp wrapper to the RoCELL implementation in posterior. The wrapper functions are called by aistats-article-figures.R.

• The folder scripts contains the script aistats-article-figures.R, which can be run from R to produce the figures in the AISTATS 2017 article and a basic script (run_IV_example.sh) for verifying that MultiNest was installed correctly.

• The top folder also contains the following files:

  • DESCRIPTION is the file describing the R package
  • NAMESPACE is the file specifying the fucntions provided by the R package
  • LICENSE.md is the file containing the GPL-3 license

Prerequisites

Just for installing the software and running basic examples, you will need:

• GNU Bash
• GNU Make Build Tool
• GNU C++ Compiler or similar
• GNU Fortran Compiler or similar
• Armadillo C++ Library for Linear Algebra
• LAPACK Linear Algebra Package
• OpenBLAS Linear Algebra Kernels
• Boost C++ Libraries

These prerequisites are routinely available for Linux-based environments. For example, the (missing) prerequisites can be installed on Ubuntu using the following terminal command: sudo apt install make lib{armadillo|boost|lapack|openblas}-dev.

This software can also run on Windows, but typically requires quite a bit more work for installing all the prerequisites. The simplest way to make it work would be to install the most recent version of the Windows Subsystem for Linux (WSL). The WSL can be used to emulate a Linux distribution like Ubuntu on a machine running Windows, so the prerequisites can be installed as if in a Linux-based environment.

A slightly more complicated but workable and native solution would be to employ the MSYS2 build platform. This setup involves installing packages (including necessary dependencies) from the MSYS2 collection: pacman -S mingw-w64-x86_64-{armadillo|boost|gcc-fortran|hdf5|lapack|openblas}.

For installing and running the RoCELL R package, a few R packages are also required. These are specified in the package DESCRIPTION file.

Installation Instructions

Download the software from GitHub with the following command: git clone --recurse-submodules https://github.com/igbucur/RoCELL.git.

Build the MultiNest nested sampling program and RoCELL by simply running make in the root directory. It is likely that some compiler flags will have to be set individually, so that the C++ compiler, e.g., gcc, can find the required headers and libraries. On Windows, the path to the collection of prerequisites must be specified separately. For example, if using MSYS2 with 64-bit MinGW, then one must add -I/c/msys64/mingw64/include to CFLAGS and -L/c/msys64/mingw64/lib to LDFLAGS in the Makefile, assuming default installation directories.

To verify that RoCELL has been built successfully, one can use the provided script by running bash scripts/run_IV_example.sh in a Linux or Windows terminal (requires GNU Bash). To verify that the R package is installed successfully, run Rscript scripts/aistats-article-figures.R.

Licensing

RoCELL algorithm - Robust Causal Estimation in the Large-Sample Limit without Strict Faithfulness

Copyright (C) 2020 Ioan Gabriel Bucur ioan.gabriel.bucur@gmail.com

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.