论文标题
在错误指定下的因果推断：根据倾向评分进行调整
Causal inference under mis-specification: adjustment based on the propensity score
论文作者
论文摘要
我们通过倾向评分回归研究贝叶斯推断的贝叶斯方法。关于倾向得分方法的许多贝叶斯文献都依赖于在常规的“以前”后推断的情况下无法将其视为完全贝叶斯的方法。此外，大多数方法都依赖于参数和分布假设，并推定正确的规范。我们强调的是，因果推论通常是在错误指定的环境中进行的，并制定了反映这一点的完全贝叶斯推论的策略。我们专注于基于决策理论论点的方法，并展示基于损失最小化的推论如何提供有效和完全的贝叶斯推论。我们基于贝叶斯引导程序提出了一种计算方法，该方法具有良好的贝叶斯和频繁特性。
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.
