Computer Science > Machine Learning
[Submitted on 5 Sep 2023 (v1), last revised 8 Jan 2024 (this version, v2)]
Title:s-ID: Causal Effect Identification in a Sub-Population
View PDF HTML (experimental)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.
Submission history
From: Amir Mohammad Abouei [view email][v1] Tue, 5 Sep 2023 14:43:10 UTC (46 KB)
[v2] Mon, 8 Jan 2024 22:31:19 UTC (50 KB)
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