Modeling Heterogeneous Treatment Effects in Survey Experiments with Bayesian Additive Regression Trees
Modeling Heterogeneous Treatment Effects in Survey Experiments with Bayesian Additive Regression Trees
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DOI：
10.1093/poq/nfs036
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发表时间：
2012-09-01
影响因子：
3.4
通讯作者：
Kern, Holger L.
中科院分区：
文献类型：
--
作者：
Green, Donald P.;Kern, Holger L.
调查实验者通常通过使用治疗指标和协变量之间的相互作用项来测试系统性变化的治疗效果。参数模型，如线性或逻辑回归，目前用于搜索系统的治疗效果异质性，但有几个缺点;特别是，由于模型错误指定和大量的自由裁量权，他们引入到实验数据的分析偏倚的可能性。在这里，我们解释了我们认为更好的方法。利用统计学习文献，我们讨论了贝叶斯加性回归树（BART），一种分析治疗效果异质性的方法。BART自动检测非线性关系和相互作用，从而减少研究人员在分析实验数据时的自由裁量权。这些功能使BART成为一个有吸引力的现成的工具，调查实验者谁想要一个灵活和强大的方式建模系统的治疗效果异质性。为了说明如何BART可以用来检测和模型异质性的治疗效果，我们重新分析了一个著名的调查实验福利的态度，从综合社会调查。
Survey experimenters routinely test for systematically varying treatment effects by using interaction terms between the treatment indicator and covariates. Parametric models, such as linear or logistic regression, are currently used to search for systematic treatment effect heterogeneity but suffer from several shortcomings; in particular, the potential for bias due to model misspecification and the large amount of discretion they introduce into the analysis of experimental data. Here, we explicate what we believe to be a better approach. Drawing on the statistical learning literature, we discuss Bayesian Additive Regression Trees (BART), a method for analyzing treatment effect heterogeneity. BART automates the detection of nonlinear relationships and interactions, thereby reducing researchers' discretion when analyzing experimental data. These features make BART an appealing off-the-shelf tool for survey experimenters who want to model systematic treatment effect heterogeneity in a flexible and robust manner. In order to illustrate how BART can be used to detect and model heterogeneous treatment effects, we reanalyze a well-known survey experiment on welfare attitudes from the General Social Survey.