Causal inference under mis-specification: adjustment based on the propensity score 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/10.48550/arxiv.2201.12831

We study Bayesian approaches to causal inference via propensity score regression. Much of the Bayesian literature on propensity score methods have relied on approaches that cannot be viewed as fully Bayesian in the context of conventional `likelihood times prior' posterior inference; in addition, most methods rely on parametric and distributional assumptions, and presumed correct specification. 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 Causal inference Propensity score matching Bayesian probability Computer science Fiducial inference Bayesian statistics Bayesian linear regression Context (archaeology) Econometrics Machine learning Artificial intelligence Mathematics Statistics Paleontology Biology

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2201.12831
PDF: https://arxiv.org/pdf/2201.12831
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Cited By: 5
Related Works: 10
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DOI: https://doi.org/10.48550/arxiv.2201.12831

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Title: Causal inference under mis-specification: adjustment based on the propensity score

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Language: en

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Publication year: 2022

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Publication date: 2022-01-30

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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://arxiv.org/abs/2201.12831

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

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Cited by: 5

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Citations by year (recent): 2023: 3, 2022: 2

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