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Comparing Covariate Prioritization via Matching to Machine Learning Methods for Causal Inference Using Five Empirical Applications
The American Statistician ( IF 1.8 ) Pub Date : 2021-02-04 , DOI: 10.1080/00031305.2020.1867638
Luke Keele 1 , Dylan S. Small 1
Affiliation  

Abstract

When investigators seek to estimate causal effects, they often assume that selection into treatment is based only on observed covariates. Under this identification strategy, analysts must adjust for observed confounders. While basic regression models have long been the dominant method of statistical adjustment, methods based on matching or weighting have become more common. Of late, methods based on machine learning (ML) have been developed for statistical adjustment. These ML methods are often designed to be black box methods with little input from the researcher. In contrast, matching methods that use covariate prioritization are designed to allow for direct input from substantive investigators. In this article, we use a novel research design to compare matching with covariate prioritization to black box methods. We use black box methods to replicate results from five studies where matching with covariate prioritization was used to customize the statistical adjustment in direct response to substantive expertise. We compare the methods in terms of both point and interval estimation. We conclude with advice for investigators.



中文翻译:

使用五个实证应用通过匹配机器学习方法进行因果推理比较协变量优先级

摘要

当研究人员试图估计因果效应时,他们通常假设治疗的选择仅基于观察到的协变量。在这种识别策略下,分析师必须针对观察到的混杂因素进行调整。虽然基本回归模型长期以来一直是统计调整的主要方法,但基于匹配或加权的方法变得越来越普遍。最近,已经开发了基于机器学习 (ML) 的方法进行统计调整。这些 ML 方法通常被设计为黑盒方法,研究人员的投入很少。相比之下,使用协变量优先排序的匹配方法旨在允许实质性调查人员的直接输入。在本文中,我们使用一种新颖的研究设计将协变量优先化匹配与黑盒方法进行比较。我们使用黑盒方法来复制五项研究的结果,其中使用协变量优先级匹配来定制统计调整,以直接响应实质性专业知识。我们在点估计和区间估计方面比较了这些方法。我们以对调查人员的建议结束。

更新日期:2021-02-04
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