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Sims, Nicholas A.

doi:10.1080/003585399108207

Cited by 1 publication

(2 citation statements)

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“…But this assumption is not always plausible in real applications. Various priors that do not rely on this assumption have been proposed [47,[63][64][65]. Below we show two simple examples.…”

Section: (B) Dependent Priorsmentioning

confidence: 99%

“…Such a phenomenon is referred to as prior dogmaticism and is the Bayesian analogue of the aforementioned problem in Ritov et al [63]. This line of research highlighted the importance of incorporating the propensity score in Bayesian causal inference [7,62,64,65], which echos the insights from the Frequentist double machine learning method [66,67]. Specifically, the regularized propensity score model or outcome model alone would not be sufficient for valid causal inference, but combining the two would achieve desirable convergence rate and finite sample performance in high-dimensional causal analysis.…”

Section: Model Specificationmentioning

confidence: 99%

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Bayesian causal inference: a critical review

Ding

Mealli

2023

Phil. Trans. R. Soc. A.

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This paper provides a critical review of the Bayesian perspective of causal inference based on the potential outcomes framework. We review the causal estimands, assignment mechanism, the general structure of Bayesian inference of causal effects and sensitivity analysis. We highlight issues that are unique to Bayesian causal inference, including the role of the propensity score, the definition of identifiability, the choice of priors in both low- and high-dimensional regimes. We point out the central role of covariate overlap and more generally the design stage in Bayesian causal inference. We extend the discussion to two complex assignment mechanisms: instrumental variable and time-varying treatments. We identify the strengths and weaknesses of the Bayesian approach to causal inference. Throughout, we illustrate the key concepts via examples. This article is part of the theme issue ‘Bayesian inference: challenges, perspectives, and prospects’.

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Section: (B) Dependent Priorsmentioning

confidence: 99%