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Activity Number: 522 - Contributed Poster Presentations: Biometrics Section
Type: Contributed
Date/Time: Wednesday, July 31, 2019 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #306870
Title: Robust Inference on the Causal Effects of Stochastic Interventions Under Two-Phase Sampling, with Applications in Vaccine Efficacy Trials
Author(s): Nima Hejazi*
Companies: UC Berkeley
Keywords: stochastic intervention; two-phase sampling; causal inference; targeted learning; vaccine efficacy; variable importance

Stochastic interventions provide a promising solution to fundamental issues posed by non-identification and inefficiency in causal inference, by allowing for the counterfactual intervention distribution to be defined as a function of its natural distribution. While such approaches are promising, real data analyses are often further complicated by economic constraints, such as when the primary variable of interest is far more expensive to collect than auxiliary covariates. Two-phase sampling schemes offer a promising solution --- unfortunately, their use produces side effects that require further adjustment when inference remains the principal goal. We present a novel approach for use in such settings: An augmented targeted minimum loss-based estimator for the causal effects of stochastic interventions, with guarantees of consistency, efficiency, and multiple robustness even in the presence of two-phase sampling. Using data from a recent HIV vaccine efficacy trial, we construct a technique that provides a highly interpretable variable importance measure for ranking multiple immune responses based on their utility as immunogenicity study endpoints in future HIV-1 vaccine trials.

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

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