s-ID: Causal Effect Identification in a Sub-population
DOI:
https://doi.org/10.1609/aaai.v38i18.30011Keywords:
RU: Causality, KRR: Action, Change, and Causality, ML: Causal Learning, RU: Probabilistic InferenceAbstract
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.Downloads
Published
2024-03-24
How to Cite
Abouei, A. M., Mokhtarian, E., & Kiyavash, N. (2024). s-ID: Causal Effect Identification in a Sub-population. Proceedings of the AAAI Conference on Artificial Intelligence, 38(18), 20302-20310. https://doi.org/10.1609/aaai.v38i18.30011
Issue
Section
AAAI Technical Track on Reasoning under Uncertainty