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Activity Number: 296
Type: Topic Contributed
Date/Time: Tuesday, August 2, 2016 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Epidemiology
Abstract #318546 View Presentation
Title: Lasso Adjustments of Treatment Effect Estimates in Randomized Experiments
Author(s): Adam Bloniarz* and Cun-Hui Zhang and Hanzhong Liu and Jasjeet Sekhon and Bin Yu
Companies: University of California at Berkeley and Rutgers University and University of California at Berkeley and University of California at Berkeley and University of California at Berkeley
Keywords: Lasso ; Causal inference ; High-dimensional statistics ; Randomized trials ; Neyman-Rubin model

We provide a principled way for investigators to analyze randomized experiments when the number of covariates is large. Investigators often use linear multivariate regression to analyze randomized experiments instead of simply reporting the difference of means between treatment and control groups. Their aim is to reduce the variance of the estimated treatment effect by adjusting for covariates. If there are a large number of covariates relative to the number of observations, regression may perform poorly because of overfitting. In such cases, the Lasso may be helpful. We study the resulting Lasso-based treatment effect estimator under the Neyman-Rubin model of randomized experiments. We present theoretical conditions that guarantee that the estimator is more efficient than the simple difference-of-means estimator, and we provide a conservative estimator of the asymptotic variance, which can yield tighter confidence intervals than the difference-of-means estimator.

Authors who are presenting talks have a * after their name.

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