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Activity Number: 494 - Identifying and Addressing Sources of Bias in Causal Inference
Type: Topic Contributed
Date/Time: Wednesday, July 31, 2019 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #304872 Presentation
Title: Causal Mediation Analysis for Stochastic Interventions
Author(s): Ivan Diaz*
Companies: Weill Medical College, Cornell University
Keywords: Mediation; Stochastic interventions; Cross-fitting

Mediation analysis in causal inference has traditionally focused on binary treatment regimes and deterministic interventions. In this talk we present an analogous decomposition of the population intervention effect, defined through stochastic interventions. Population intervention effects provide a generalized framework in which a variety of interesting causal contrasts can be defined, including effects for continuous and categorical exposures. We show that identification of direct and indirect effects for the population intervention effect requires weaker assumptions than its average treatment effect counterpart. In particular, identification of direct effects is guaranteed in experiments that randomize the treatment and the mediator. We discuss various estimators of the direct and indirect effects, including substitution, re-weighted, and efficient estimators based on flexible regression techniques. Our efficient estimator is asymptotically linear under a condition requiring quartic consistency of certain regression functions.

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

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