@inproceedings{
abouei2024causal,
title={Causal Effect Identification in a Sub-Population with Latent Variables},
author={Amir Mohammad Abouei and Ehsan Mokhtarian and Negar Kiyavash and Matthias Grossglauser},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=iEsyRsg6t1}
}
The s-ID problem seeks to compute a causal effect in a specific sub-population from the observational data pertaining to the same sub population (Abouei et al., 2023). This problem has been addressed when all the variables in the system are observable. In this paper, we consider an extension of the s-ID problem that allows for the presence of latent variables. To tackle the challenges induced by the presence of latent variables in a sub-population, we first extend the classical relevant graphical definitions, such as c-components and Hedges, initially defined for the so-called ID problem (Pearl, 1995; Tian & Pearl, 2002), to their new counterparts. Subsequently, we propose a sound algorithm for the s-ID problem with latent variables.
Causal Effect Identification in a Sub-Population with Latent Variables
Amir Mohammad Abouei Ehsan Mokhtarian Negar Kiyavash Matthias Grossglauser