Causal Inference Under Mis-Specification: Adjustment Based on the Propensity Score (with Discussion) Article Swipe

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David A. Stephens , Widemberg S. Nobre , Erica E. M. Moodie , Alexandra M. Schmidt ·

YOU? · · 2022 · Open Access · · DOI: https://doi.org/10.1214/22-ba1322

We study Bayesian approaches to causal inference via propensity score regression. Much of Bayesian methodology relies on parametric and distributional assumptions, with presumed correct specification, whereas the extant propensity score methods in Bayesian literature have relied on approaches that cannot be viewed as fully Bayesian in the context of conventional 'likelihood times prior' posterior inference. We emphasize that causal inference is typically carried out in settings of mis-specification, and develop strategies for fully Bayesian inference that reflect this. We focus on methods based on decision-theoretic arguments, and show how inference based on loss-minimization can give valid and fully Bayesian inference. We propose a computational approach to inference based on the Bayesian bootstrap which has good Bayesian and frequentist properties.

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Frequentist inference Inference Bayesian inference Bayesian probability Computer science Causal inference Bayesian statistics Propensity score matching Fiducial inference Machine learning Bayesian linear regression Artificial intelligence Econometrics Mathematics Statistics

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Type: article
Language: en
Landing Page: https://doi.org/10.1214/22-ba1322
PDF: https://projecteuclid.org/journals/bayesian-analysis/advance-publication/Causal-Inference-Under-Mis-Specification--Adjustment-Based-on-the/10.1214/22-BA1322.pdf
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Cited By: 9
References: 47
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DOI: https://doi.org/10.1214/22-ba1322

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Title: Causal Inference Under Mis-Specification: Adjustment Based on the Propensity Score (with Discussion)

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Language: en

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Publication year: 2022

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Publication date: 2022-07-27

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Authors: David A. Stephens, Widemberg S. Nobre, Erica E. M. Moodie, Alexandra M. Schmidt

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Landing page: https://doi.org/10.1214/22-ba1322

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Concepts: Frequentist inference, Inference, Bayesian inference, Bayesian probability, Computer science, Causal inference, Bayesian statistics, Propensity score matching, Fiducial inference, Machine learning, Bayesian linear regression, Artificial intelligence, Econometrics, Mathematics, Statistics

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Cited by: 9

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