This repository contains the code to implement examples of CoCo as described in Optimization-based Causal Estimation from Heterogenous Environments by Mingzhang Yin, Yixin Wang and David Blei.
This paper presents an optimization approach to causal estimation. Given data that contains covariates and an outcome, which covariates are causes of the outcome, and what is the strength of the causality? In classical machine learning, the goal of optimization is to maximize predictive accuracy. However, some covariates might exhibit non-causal association to the outcome. Such spurious associations provide predictive power for classical ML, but they prevent us from interpreting the result causally. This paper proposes CoCo, an optimization algorithm that bridges the gap between pure prediction and causal inference. CoCo leverages the recently-proposed idea of environments, datasets of covariates/response where the causal relationships remain invariant but where the distribution of the covariates changes from environment to environment. Given datasets from multiple environments---and ones that exhibit sufficient heterogeneity---CoCo maximizes an objective for which the only solution is the causal solution. We describe the theoretical foundations of this approach and demonstrate its effectiveness on simulated and real datasets. Compared to classical ML and the recently-proposed IRMv1, CoCo provides more accurate estimates of the causal model.
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The python scripts are included in the folder
src/. The bash files to submit jobs to the cluster are included asrun.sh(note that this depends on the machine and the bash file syntax may need to be changed accordingly). -
To generate data for wild animal classification, follow the steps describe in this repo. Other data can be self generated or auto downloaded.
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To run the code
cd ./src/exp_xxx
chmod +x run.sh
bash run.sh
- Below is the bibliography information of the paper:
@article{yin2021optimization,
title={Optimization-based Causal Estimation from Heterogenous Environments},
author={Mingzhang Yin and Yixin Wang and David M. Blei},
year={2021},
journal={arXiv preprint arXiv:2109.11990}
}