因果效应贝叶斯估计实用导论：参数与非参数方法
A Practical Introduction to Bayesian Estimation of Causal Effects: Parametric and Nonparametric Approaches
AI总结：
本文面向统计实践者系统介绍因果效应的贝叶斯估计，涵盖参数先验收缩、稀疏性、概率敏感性分析及非参数贝叶斯方法，并提供点处理、时变处理与开源代码示例。
摘要：
近年来，用于因果推断的贝叶斯方法取得了实质性进展。本文面向已对贝叶斯模型有一定了解、并希望概览其在实际场景中能为因果估计增添何种价值的执业统计学家，介绍因果效应的贝叶斯推断。文中展示先验如何在参数模型上诱导收缩与稀疏性，并用于围绕因果假设开展概率敏感性分析。我们概述非参数贝叶斯估计，并综述其在因果推断文献中的应用。文中考虑了点处理和时变处理情形下的推断；对于后者，探讨静态和动态处理机制。全文使用现成的开源软件说明实现过程。我们希望读者能够获得使用参数与非参数模型进行贝叶斯因果推断的实现层面知识。文中使用的所有合成示例和代码均可在配套 GitHub 仓库公开获取。
英文摘要：
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.
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