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Activity Number: 35 - Applications of Nonparametric Methods
Type: Contributed
Date/Time: Sunday, July 28, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Nonparametric Statistics
Abstract #304322
Title: Sufficient Dimension Reduction for Feasible and Robust Estimation of Average Causal Effect
Author(s): Trinetri Ghosh* and Yanyuan Ma and Xavier de Luna
Companies: Pennsylvania State University and The Pennsylvania State University and Umeå School of Business,Economics and Statistics at Umeå University
Keywords: Average Treatment Effect; Double Robust Estimator; Efficiency; Inverse Probability Weighting; Shrinkage Estimator.

When estimating the treatment effect in an observational study, we use a semiparametric locally efficient dimension reduction approach to assess both the treatment assignment mechanism and the average responses in both treated and non-treated groups. We then integrate all results through imputation, inverse probability weighting and double robust augmentation estimators. Double robust estimators are locally efficient while imputation estimators are super-efficient when the response models are correct. To take advantage of both procedures, we introduce a shrinkage estimator to automatically combine the two, which retains the double robustness property while improving on the variance when the response model is correct. We demonstrate the performance of these estimators through simulated experiments and a real dataset concerning the effect of maternal smoking on baby birth weight.

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

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