论文标题
贝叶斯因果推论：批判性评论
Bayesian Causal Inference: A Critical Review
论文作者
论文摘要
本文根据潜在结果框架对因果推断的贝叶斯观点进行了批判性审查。我们回顾了因果估计，识别假设，因果效应的贝叶斯推论的一般结构以及灵敏度分析。我们重点介绍了贝叶斯因果推断所独有的问题，包括倾向得分的作用，可识别性的定义，在低维度和高维度方面的先验选择。我们指出协变量重叠的核心作用，更普遍地是贝叶斯因果推断的设计阶段。我们将讨论扩展到两种复杂的分配机制：仪器变量和时变的处理。在整个过程中，我们通过示例说明了关键概念。
This paper provides a critical review of the Bayesian perspective of causal inference based on the potential outcomes framework. We review the causal estimands, identification assumptions, 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, 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. Throughout, we illustrate the key concepts via examples.
