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Activity Number: 108 - Can't Shake That Feeling That You're Missing Something?
Type: Invited
Date/Time: Monday, August 3, 2020 : 1:00 PM to 2:50 PM
Sponsor: Health Policy Statistics Section
Abstract #308072
Title: Bootstrapping Sensitivity Analysis for Inverse Probability Weighting Estimators
Author(s): Qingyuan Zhao* and Dylan Small and Bhaswar Bhattarcharya
Companies: University of Cambridge and University of Pennsylvania and University of Pennsylvania
Keywords: causal inference; sensitivity analysis; bootstrap; optimization

To identify the estimand in missing data problems and observational studies, it is common to base the statistical estimation on the “missing at random” and “no unmeasured confounder” assumptions. However, these assumptions are unverifiable using empirical data and pose serious threats to the validity of the qualitative conclusions of the statistical inference. A sensitivity analysis asks how the conclusions may change if the unverifiable assumptions are violated to a certain degree. In this paper we consider a marginal sensitivity model which is a natural extension of Rosenbaum’s sensitivity model that is widely used for matched observational studies. We aim to construct confidence intervals based on inverse probability weighting estimators, such that asymptotically the intervals have at least nominal coverage of the estimand whenever the data generating distribution is in the collection of marginal sensitivity models. We use a percentile bootstrap and a generalized minimax/maximin inequality to transform this intractable problem to a linear fractional programming problem, which can be solved very efficiently. We illustrate our method using a real dataset.

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

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