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Activity Number: 139 - Recent Advances of Semi-Supervised Learning: Techniques and Applications
Type: Invited
Date/Time: Tuesday, August 10, 2021 : 10:00 AM to 11:50 AM
Sponsor: Section on Statistical Learning and Data Science
Abstract #316845
Title: Doubly Robust Semi-Supervised Inference for Means Under MAR-Type Labeling Mechanisms with Selection Bias and Decaying Propensity Scores
Author(s): Abhishek Chakrabortty*
Companies: Texas A&M University
Keywords: Semi-supervised inference; Selection/labeling bias and MAR mechanisms; Extreme missingness and non-standard asymptotics; Doubly robust estimation; Decaying/imbalanced propensity score modeling; Average treatment effects
Abstract:

Semi-supervised (SS) inference has received a lot of attention in recent times. In SS settings, apart from a moderate sized labeled data L, one has a much larger sized unlabeled data U available with |U| >> |L|, which makes them unique and different from standard missing data problems. However, most of the SS literature implicitly assumes L and U to be equally distributed. There is hardly any work under missing at random (MAR) type labeling with selection bias, which is far more realistic but also quite challenging due to the inevitably decaying nature of the propensity score (PS) here. To address this major gap, we consider SS estimation of the mean response under such MAR settings. We develop a SS double robust (DR) estimator as an adaptation of traditional DR estimators to this extreme setting. We give a complete characterization of its asymptotic properties through a series of results requiring only high-level conditions on the nuisance estimators. Lastly, another key challenge is to model the decaying PS for which we propose several novel choices and provide detailed results on their properties under both high and low dimensional settings. These may be of independent interest.


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

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