s-ID: Causal Effect Identification in a Sub-population
Amir Mohammad Abouei, Ehsan Mokhtarian, Negar Kiyavash
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.
BibTeX
@article{Abouei_Mokhtarian_Kiyavash_2024, title={s-ID: Causal Effect Identification in a Sub-population}, volume={38}, url={https://ojs.aaai.org/index.php/AAAI/article/view/30011}, DOI={10.1609/aaai.v38i18.30011}, abstractNote={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.}, number={18}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Abouei, Amir Mohammad and Mokhtarian, Ehsan and Kiyavash, Negar}, year={2024}, month={Mar.}, pages={20302-20310} }