Rebar: Reinforcing a Matching Estimator With Predictions From High-Dimensional Covariates
Rebar: Reinforcing a Matching Estimator With Predictions From High-Dimensional Covariates
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Rebar：通过高维协变量的预测强化匹配估计器
DOI：
10.3102/1076998617731518
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发表时间：
2015
影响因子：
2.4
通讯作者：
Brian Rowan
中科院分区：
文献类型：
--
作者：
Adam C. Sales;B. Hansen;Brian Rowan
在因果匹配设计中，一些控制对象经常是不匹配的，一些协变量经常是未建模的。本文介绍了“rebar”，这是一种使用高维建模来合并这些通常被丢弃的数据而不牺牲匹配设计的完整性的方法。在构建匹配后，研究人员使用不匹配的控制对象（剩余）来拟合机器学习模型，预测控制潜在结果作为完整协变量矩阵的函数。匹配集中的结果预测用于调整因果估计以减少混杂偏倚。我们提出了理论结果来证明该方法的偏差减少特性，并通过模拟研究证明了这些特性。此外，我们说明了在亚利桑那州的一所学校一级的综合教育改革计划的评估方法。
In causal matching designs, some control subjects are often left unmatched, and some covariates are often left unmodeled. This article introduces “rebar,” a method using high-dimensional modeling to incorporate these commonly discarded data without sacrificing the integrity of the matching design. After constructing a match, a researcher uses the unmatched control subjects—the remnant—to fit a machine learning model predicting control potential outcomes as a function of the full covariate matrix. The resulting predictions in the matched set are used to adjust the causal estimate to reduce confounding bias. We present theoretical results to justify the method’s bias-reducing properties as well as a simulation study that demonstrates them. Additionally, we illustrate the method in an evaluation of a school-level comprehensive educational reform program in Arizona.
影响因子：
5
作者：
Brookhart, M. Alan;Schneeweiss, Sebastian;Sturmer, Til
通讯作者：
Sturmer, Til
DOI：
10.1214/07-sts227b
发表时间：
2007-01-01
期刊：
Statistical science : a review journal of the Institute of Mathematical Statistics
影响因子：
--
作者：
Tsiatis, Anastasios A;Davidian, Marie
通讯作者：
Davidian, Marie