Covariate selection with group lasso and doubly robust estimation of causal effects.
Covariate selection with group lasso and doubly robust estimation of causal effects.
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DOI：
10.1111/biom.12736
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发表时间：
2018-03
期刊：
影响因子：
1.9
通讯作者：
Wolfson J
中科院分区：
文献类型：
--
作者：
Koch B;Vock DM;Wolfson J
通过在治疗和结果模型中只包括与治疗和结果相关的协变量（即混杂因素）或仅与结果相关的协变量，可以提高治疗的平均因果效应（ACE）的双稳健估计器的效率。然而，在给定的研究中可能测量的大量协变量中识别这些协变量通常是具有挑战性的。在本文中，我们提出了GLiDeR (Group Lasso and double Robust Estimation)，这是一种新的变量选择技术，用于识别混杂因素和结果预测因素，使用自适应组套索方法，同时在治疗和结果模型中执行系数选择、正则化和估计。选择的变量和相应的系数估计用于标准的双鲁棒ACE估计。我们提供的渐近结果表明，对于一类广泛的数据生成机制，当结果或治疗模型被正确指定时，GLiDeR产生ACE的一致估计。一项全面的仿真研究表明，GLiDeR比使用标准变量选择技术的双鲁棒方法更有效，并且比最近提出的双鲁棒贝叶斯模型平均方法具有显著的计算优势。我们通过观察性登记估计双肺移植与单肺移植对移植后一年内用力呼气量的因果治疗效果来说明我们的方法。
The efficiency of doubly robust estimators of the average causal effect (ACE) of a treatment can be improved by including in the treatment and outcome models only those covariates which are related to both treatment and outcome (i.e., confounders) or related only to the outcome. However, it is often challenging to identify such covariates among the large number that may be measured in a given study. In this paper, we propose GLiDeR (Group Lasso and Doubly Robust Estimation), a novel variable selection technique for identifying confounders and predictors of outcome using an adaptive group lasso approach that simultaneously performs coefficient selection, regularization, and estimation across the treatment and outcome models. The selected variables and corresponding coefficient estimates are used in a standard doubly robust ACE estimator. We provide asymptotic results showing that, for a broad class of data generating mechanisms, GLiDeR yields a consistent estimator of the ACE when either the outcome or treatment model is correctly specified. A comprehensive simulation study shows that GLiDeR is more efficient than doubly robust methods using standard variable selection techniques and has substantial computational advantages over a recently proposed doubly robust Bayesian model averaging method. We illustrate our method by estimating the causal treatment effect of bilateral versus single-lung transplant on forced expiratory volume in one year after transplant using an observational registry.
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