Oganisian Arman, Roy Jason A
Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Department of Biostatistics and Epidemiology, Rutgers University, New Brunswick, New Jersey, USA.
Stat Med. 2021 Jan 30;40(2):518-551. doi: 10.1002/sim.8761. Epub 2020 Oct 5.
Substantial advances in Bayesian methods for causal inference have been made 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 in 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 to leave the reader 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.
近年来，贝叶斯因果推断方法取得了重大进展。我们为那些对贝叶斯模型有一定了解并希望概述其在实际环境中因果估计方面所能提供的内容的统计学家，提供贝叶斯因果效应推断的介绍。在本文中，我们展示了先验如何在参数模型中引起收缩和稀疏性，并用于围绕因果假设进行概率敏感性分析。我们概述了非参数贝叶斯估计，并审视了它们在因果推断文献中的应用。考虑了点治疗和时变治疗设置中的推断。对于后者，我们探讨了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来说明实现过程。我们希望让读者掌握使用参数和非参数模型进行贝叶斯因果推断的实现层面的知识。本文中使用的所有合成示例和代码都可在配套的GitHub存储库上公开获取。
