\citet{Rosenbaum83ps} introduced the notion of propensity score and discussed its central role in causal inference with observational studies. Their paper, however, causes a fundamental incoherence with an early paper by \citet{Rubin78}, which showed that the propensity score does not play any role in the Bayesian analysis of unconfounded observational studies if the priors on the propensity score and outcome models are independent. Despite the serious efforts made in the literature, it is generally difficult to reconcile these contradicting results. We offer a simple approach to incorporating the propensity score in Bayesian causal inference based on the posterior predictive $p$-value for the model with the strong null hypothesis of no causal effects for any units whatsoever. Computationally, the proposed posterior predictive $p$-value equals the classic $p$-value based on the Fisher randomization test averaged over the posterior predictive distribution of the propensity score. Moreover, using the studentized doubly robust estimator as the test statistic, the proposed $p$-value inherits the doubly robust property and is also asymptotically valid for testing the weak null hypothesis of zero average causal effect. Perhaps surprisingly, this Bayesianly motivated $p$-value can have better frequentist's finite-sample performance than the frequentist's $p$-value based on the asymptotic approximation especially when the propensity scores can take extreme values.
翻译:{\fn方正黑体简体\fs18\b1\bord1\shad1\3cH2F2F2F}\cH2F2F2F}\fs12\bord1\shad1\3cH2F2F2F}\citet{Rubin78} 引入了偏差评分的概念,并讨论了其在因果推论中的核心作用。然而,他们的论文却造成一种根本的不一致,因为它与由\citet{Rubin78}撰写的早期论文的早期论文基本不一致。这表明,如果偏差和结果模型上的前科是独立的,那么,这种偏差评分在巴伊斯对无根据的观察研究的分析中没有任何作用。尽管在文献中做出了认真努力,但通常很难调和这些相互矛盾的结果。我们提供了一个简单的方法,将贝伊斯的偏差评分纳入贝亚的偏差值中。基于对模型的后半价预测值$-价值的预测与任何单位都没有因果关系的强烈假设。 比较,拟议的后值预测值与根据渔业随机随机测测算得出的典型的美元平均价值相等。 此外,用学生的稳性定的直值作为定期估的直值,更精确的估值,这更能的直估的估值也是的精确的估值。