s-ID: Causal Effect Identification in a Sub-Population

This repository implements the s-ID algorithm in "s-ID: Causal Effect Identification in a Sub-Population."

Abstract

Causal inference in a sub-population involves identifying the causal effect of an intervention on a specific subgroup, which is distinguished from the whole population through the influence of systematic biases in the sampling process. However, ignoring the subtleties introduced by sub-populations can either lead to erroneous inference or limit the applicability of existing methods. We introduce and advocate for a causal inference problem in sub-populations (henceforth called s-ID), in which we merely have access to observational data of the targeted sub-population (as opposed to the entire population). Existing inference problems in sub-populations operate on the premise that the given data distributions originate from the entire population, thus, cannot tackle the s-ID problem. To address this gap, we provide necessary and sufficient conditions that must hold in the causal graph for a causal effect in a sub-population to be identifiable from the observational distribution of that sub-population. Given these conditions, we present a sound and complete algorithm for the s-ID problem.

Contents

• main.py: This file consists of the main algorithm and utils for working with graphs.

• examples.py: This file includes some examples in the paper.

• requirements.txt: Requirements packages for running the code.

Install

For installing the dependencies, run the following code

pip install -r requirements.txt

How to use the algorithm

• First, you need to create a dag using the function create_dag.
• Then, call s-ID to identify your desired causal effect.

Cite

If you find this code useful in your research, please consider citing:

@misc{abouei2024sid,
      title={s-ID: Causal Effect Identification in a Sub-Population}, 
      author={Amir Mohammad Abouei and Ehsan Mokhtarian and Negar Kiyavash},
      year={2024},
      eprint={2309.02281},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

Contact

Please get in touch with amir.abouei@epfl.ch in case you have questions regarding the code.