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Regression adjustment for treatment effect with multicollinearity in high dimensions

Lili Yue, Gaorong Li, Heng Lian and Xiang Wan

Computational Statistics & Data Analysis, 2019, vol. 134, issue C, 17-35

Abstract: Randomized experiment is an important tool for studying the Average Treatment Effect (ATE). This paper considers the regression adjustment estimation of the Sample Average Treatment Effect (SATE) in high-dimensional case, where the multicollinearity problem is often encountered and needs to be properly handled. Many existing regression adjustment methods fail to achieve satisfactory performances. To solve this issue, an Elastic-net adjusted estimator for SATE is proposed under the Rubin causal model of randomized experiments with multicollinearity in high dimensions. The asymptotic properties of the proposed SATE estimator are shown under some regularity conditions, and the asymptotic variance is proved to be not greater than that of the unadjusted estimator. Furthermore, Neyman-type conservative estimators for the asymptotic variance are proposed, which yields tighter confidence intervals than both the unadjusted and the Lasso-based adjusted estimators. Some simulation studies are carried out to show that the Elastic-net adjusted method is better in addressing collinearity problem than the existing methods. The advantages of our proposed method are also shown in analyzing the dataset of HER2 breast cancer patients.

Keywords: Average Treatment Effect; Causal inference; Elastic-net; High-dimensional data; Randomized experiments; Rubin causal model (search for similar items in EconPapers)
Date: 2019
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