Spurred on by recent successes in causal inference competitions, Bayesian nonparametric (and high-dimensional) methods have recently seen increased attention in the causal inference literature. In this paper, we present a comprehensive overview of Bayesian nonparametric applications to causal inference. Our aims are to (i) introduce the fundamental Bayesian nonparametric toolkit; (ii) discuss how to determine which tool is most appropriate for a given problem; and (iii) show how to avoid common pitfalls in applying Bayesian nonparametric methods in high-dimensional settings. Unlike standard fixed-dimensional parametric problems, where outcome modeling alone can sometimes be effective, we argue that most of the time it is necessary to model both the selection and outcome processes.
翻译:在因果推断竞争最近取得的成功的激励下,巴耶斯非参数(和高维)方法最近在因果推断文献中日益受到重视。在本文件中,我们全面概述了巴伊斯非参数应用对因果推断的应用。我们的目标是:(一) 介绍巴伊斯非参数基本工具包;(二) 讨论如何确定哪一种工具最适合某一特定问题;(三) 说明如何避免在高维环境中应用巴伊西亚非参数方法时常见的陷阱。与标准的固定维参数问题不同,在标准固定维参数问题上,结果模型有时可以产生效力。我们争辩说,大多数时候都有必要对选择过程和结果过程进行建模。