Abstract In the causal adjustment setting, variable selection techniques based only on the outcome or only on the treatment allocation model can result in the omission of confounders and hence may lead to bias, or the inclusion of spurious variables and hence cause variance inflation, in estimation of the treatment effect. We propose a variable selection method using a penalized objective function that is based on both the outcome and treatment assignment models. The proposed method facilitates confounder selection in high-dimensional settings. We show that under some mild conditions our method attains the oracle property. The selected variables are used to form a doubly robust regression estimator of the treatment effect. Using the proposed method we analyze a set of data on economic growth and study the effect of life expectancy as a measure of population health on the average growth rate of gross domestic product per capita.

中文翻译：

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使用同时惩罚方法的因果推理中的变量选择

摘要 在因果调整设置中，仅基于结果或仅基于治疗分配模型的变量选择技术会导致在估计中遗漏混杂因素从而导致偏差，或包含虚假变量从而导致方差膨胀。的治疗效果。我们提出了一种使用基于结果和治疗分配模型的惩罚目标函数的变量选择方法。所提出的方法有助于在高维设置中选择混杂因素。我们表明，在一些温和的条件下，我们的方法达到了预言机属性。所选变量用于形成治疗效果的双重稳健回归估计量。