s-ID: Causal Effect Identification in a Sub-Population

• URL: http://arxiv.org/abs/2309.02281v2
• Date: Mon, 8 Jan 2024 22:31:19 GMT
• Title: s-ID: Causal Effect Identification in a Sub-Population
• Authors: Amir Mohammad Abouei, Ehsan Mokhtarian, Negar Kiyavash
• Abstract summary: We introduce and advocate for a causal inference problem in sub-populations (henceforth called s-ID) Existing inference problems in sub-populations operate on the premise that the given data originate from the entire population. 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.
• Score: 23.221279036710012
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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