Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects
Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects
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用于因果推理的贝叶斯回归树模型：正则化、混杂和异质效应
DOI：
10.2139/ssrn.3048177
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
C. Carvalho
中科院分区：
文献类型：
--
作者：
P. Hahn;Jared S. Murray;C. Carvalho
本文提出了一种新的非线性回归模型，用于从观察数据中估计异质性治疗效果，特别适用于效应量小，异质性效应和强混杂的情况。标准的非线性回归模型，这可能是相当不错的预测，有两个显着的弱点时，用于估计异质性的治疗效果。首先，当拟合具有强混杂的数据时，它们可能产生严重偏倚的治疗效果估计值。本文提出的贝叶斯因果森林模型避免了这个问题，直接将估计的倾向函数的规格的响应模型，隐式地诱导协变量依赖的回归函数的先验。其次，响应面建模的标准方法不能充分控制效应异质性的正则化强度。贝叶斯因果森林模型允许治疗效果异质性与控制变量的预后效果分开进行正则化，从而可以在信息上“缩小到同质性”。我们通过重新分析一项评估吸烟对医疗支出的因果影响的观察性研究以及广泛的模拟研究来说明这些好处。
This paper presents a novel nonlinear regression model for estimating heterogeneous treatment effects from observational data, geared specifically towards situations with small effect sizes, heterogeneous effects, and strong confounding. Standard nonlinear regression models, which may work quite well for prediction, have two notable weaknesses when used to estimate heterogeneous treatment effects. First, they can yield badly biased estimates of treatment effects when fit to data with strong confounding. The Bayesian causal forest model presented in this paper avoids this problem by directly incorporating an estimate of the propensity function in the specification of the response model, implicitly inducing a covariate-dependent prior on the regression function. Second, standard approaches to response surface modeling do not provide adequate control over the strength of regularization over effect heterogeneity. The Bayesian causal forest model permits treatment effect heterogeneity to be regularized separately from the prognostic effect of control variables, making it possible to informatively "shrink to homogeneity". We illustrate these benefits via the reanalysis of an observational study assessing the causal effects of smoking on medical expenditures as well as extensive simulation studies.
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DOI：
10.1080/10618600.2019.1677243
发表时间：
2020-04
影响因子：
2.4
作者：
M. Pratola;H. Chipman;Edward I. George;R. McCulloch
通讯作者：
M. Pratola;H. Chipman;Edward I. George;R. McCulloch
影响因子：
5
作者：
Cole, Stephen R.;Hernan, Miguel A.
通讯作者：
Hernan, Miguel A.
影响因子：
3
作者：
Motonobu Kanagawa;Bharath K. Sriperumbudur;K. Fukumizu
通讯作者：
Motonobu Kanagawa;Bharath K. Sriperumbudur;K. Fukumizu
影响因子：
5.7
作者：
Ding, Peng;Li, Fan
通讯作者：
Li, Fan
影响因子：
1.9
作者：
ROBINS, JM;MARK, SD;NEWEY, WK
通讯作者：
NEWEY, WK