论文标题
贝叶斯估计因果效应的实用介绍：参数和非参数方法
A Practical Introduction to Bayesian Estimation of Causal Effects: Parametric and Nonparametric Approaches
论文作者
论文摘要
近年来，贝叶斯方法的因果推断方法取得了重大进展。我们为对贝叶斯模型有些熟悉的实践统计学家的因果效应提供了介绍，并希望概述它可以在实际环境中增加因果估计的概述。在本文中，我们证明了先验如何在参数模型上诱导收缩和稀疏性，并用于在因果假设周围进行概率敏感性分析。我们提供了非参数贝叶斯估计的概述，并调查了其在因果推理文献中的应用。考虑了点处理和时变治疗环境的推断。对于后者，我们探索静态和动态治疗方案。在整个过程中，我们使用现成的开源软件说明实施。我们希望读者能够使用参数模型和非参数模型来借助贝叶斯因果推理的实现级别知识。本文中使用的所有合成示例和代码均在伴侣GitHub存储库上公开可用。
Substantial advances in Bayesian methods for causal inference have been developed in recent years. We provide an introduction to Bayesian inference for causal effects for practicing statisticians who have some familiarity with Bayesian models and would like an overview of what it can add to causal estimation in practical settings. In the paper, we demonstrate how priors can induce shrinkage and sparsity on parametric models and be used to perform probabilistic sensitivity analyses around causal assumptions. We provide an overview of nonparametric Bayesian estimation and survey their applications in the causal inference literature. Inference in the point-treatment and time-varying treatment settings are considered. For the latter, we explore both static and dynamic treatment regimes. Throughout, we illustrate implementation using off-the-shelf open source software. We hope the reader will walk away with implementation-level knowledge of Bayesian causal inference using both parametric and nonparametric models. All synthetic examples and code used in the paper are publicly available on a companion GitHub repository.
