Penalized regression procedures for variable selection in the potential outcomes framework
Penalized regression procedures for variable selection in the potential outcomes framework
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DOI：
10.1002/sim.6433
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发表时间：
2015-05-10
影响因子：
2
通讯作者：
Coffman, Donna L.
中科院分区：
文献类型：
--
作者：
Ghosh, Debashis;Zhu, Yeying;Coffman, Donna L.
最近的一个主题是因果推理的兴趣是模型选择。在这篇文章中，我们描述了一个框架，在其中考虑惩罚回归方法的变量选择的因果效应。该框架导致一个简单的插补，然后选择的程序是不可知的类型的插补算法以及惩罚回归使用。它还阐明了模型选择如何涉及因果推理问题的多元回归模型，这些方法可用于识别治疗效果同质的亚组。与机器学习方法，缺失数据和插补的文献进行了类比和链接。定义了一个差分最小绝对收缩和选择算子算法，沿着它的多个插补类似物。使用一个众所周知的右心导管数据集的程序进行说明。版权所有（c）2015约翰威利父子有限公司
A recent topic of much interest in causal inference is model selection. In this article, we describe a framework in which to consider penalized regression approaches to variable selection for causal effects. The framework leads to a simple impute, then select' class of procedures that is agnostic to the type of imputation algorithm as well as penalized regression used. It also clarifies how model selection involves a multivariate regression model for causal inference problems and that these methods can be applied for identifying subgroups in which treatment effects are homogeneous. Analogies and links with the literature on machine learning methods, missing data, and imputation are drawn. A difference least absolute shrinkage and selection operator algorithm is defined, along with its multiple imputation analogs. The procedures are illustrated using a well-known right-heart catheterization dataset. Copyright (c) 2015 John Wiley & Sons, Ltd.