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- W4288284503 abstract "Overlap between treatment groups is required for non-parametric estimation of causal effects. If a subgroup of subjects always receives the same intervention, we cannot estimate the effect of intervention changes on that subgroup without further assumptions. When overlap does not hold globally, characterizing local regions of overlap can inform the relevance of causal conclusions for new subjects, and can help guide additional data collection. To have impact, these descriptions must be interpretable for downstream users who are not machine learning experts, such as policy makers. We formalize overlap estimation as a problem of finding minimum volume sets subject to coverage constraints and reduce this problem to binary classification with Boolean rule classifiers. We then generalize this method to estimate overlap in off-policy policy evaluation. In several real-world applications, we demonstrate that these rules have comparable accuracy to black-box estimators and provide intuitive and informative explanations that can inform policy making." @default.
- W4288284503 created "2022-07-28" @default.
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- W4288284503 date "2019-07-09" @default.
- W4288284503 modified "2023-09-25" @default.
- W4288284503 title "Characterization of Overlap in Observational Studies" @default.
- W4288284503 doi "https://doi.org/10.48550/arxiv.1907.04138" @default.
- W4288284503 hasPublicationYear "2019" @default.
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