s-ID: Causal Effect Identification in a Sub-Population
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.
2-s2.0-85189532572
2024-03-25
Proceedings of the AAAI Conference on Artificial Intelligence; 38
2374-3468
2159-5399
18
20302
20310
REVIEWED
EPFL
Event name | Event acronym | Event place | Event date |
Vancouver, Canada | 2024-02-20 - 2024-02-27 | ||