Abstract Number:
3641
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Tianyu Guo (1), Sai Praneeth Karimireddy (2), Michael Jordan (3)
Institutions:
(1) N/A, N/A, (2) UC Berkeley, N/A, (3) Univ of California-Berkeley, N/A
Co-Author(s):
First Author:
Presenting Author:
Abstract Text:
Inverse Propensity Score Weighting (\textsc{IPW}) is a fundamental methodological tool for causal inference in observational data. However, it can suffer from unbounded variance when encountering extreme propensity scores. Trimming these extremal scores is a widely used practice to avoid such a blowup of the variance, but its effect is poorly understood and remains an open theoretical problem in causal inference.
In this work, we derive the asymptotic distribution for the \textsc{IPW} estimate, using trimmed estimated propensity scores. We then show how our results can guide the choice of a trimming threshold, and systematically compare widely utilized methods for extreme propensity scores, suggesting different applicable scenarios for each method. Along the way, we develop a new technique to handle the non-smoothness in the delta method.
Keywords:
Inverse propensity score weighting|Limited overlap|Non-smoothness|Asymptotic distribution| |
Sponsors:
IMS
Tracks:
Statistical Theory
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