s-ID: Causal Effect Identification in a Sub-Population
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866914634807640064 |
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| author | Abouei, Amir Mohammad Mokhtarian, Ehsan Kiyavash, Negar |
| author_facet | Abouei, Amir Mohammad Mokhtarian, Ehsan Kiyavash, Negar |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_02281 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | s-ID: Causal Effect Identification in a Sub-Population Abouei, Amir Mohammad Mokhtarian, Ehsan Kiyavash, Negar Machine Learning Artificial Intelligence Methodology 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. |
| title | s-ID: Causal Effect Identification in a Sub-Population |
| topic | Machine Learning Artificial Intelligence Methodology |
| url | https://arxiv.org/abs/2309.02281 |