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Activity Number: 545 - Machine Learning and Nonparametric Methods in Causal Inference
Type: Topic Contributed
Date/Time: Thursday, August 6, 2020 : 1:00 PM to 2:50 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #309693
Title: Efficient Estimation of Stochastic Intervention Effects in Causal Mediation Analysis
Author(s): Nima Hejazi* and Iván Díaz and Mark Van der Laan
Companies: UC Berkeley and Weill Cornell Medicine and University of California, Berkeley
Keywords: causal inference; mediation analysis; stochastic intervention; targeted learning (TMLE); indirect effects; cross-world assumptions

Mediation analysis in causal inference has traditionally centered on static interventions and binary exposures, with classical theory introducing the natural (in)direct effects thru a decomposition of the average treatment effect. From a decomposition of the population intervention effect, defined via stochastic interventions on exposure and mediators, we outline a framework for defining a variety of interesting causal contrasts, including effects for continuous and categorical exposures. Our (in)direct effects have been shown to require weaker assumptions than their average treatment effect analogs, making them a suitable choice for settings in which the cross-world independencies of traditional mediation analysis are unverifiable. We construct and evaluate two efficient estimators of our (in)direct effects: a one-step estimator and a targeted minimum loss estimator that uniquely uses an incompatible updating procedure, both of which accommodate state-of-the-art machine learning in estimating nuisance parameters. We discuss theoretical conditions for establishing the asymptotic linearity of our efficient estimators and investigate their practical performance in simulation studies.

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

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