Primary Submission Category: Interference and Consistency Violations
Multiply Robust Estimators for Controlled Direct Effects in the Presence of Interference
Authors: Jimmy Kelliher, Nandita Mitra,
Presenting Author: Jimmy Kelliher*
Controlled direct effects are important causal estimands for public health scientists and policymakers interested in understanding the mechanisms by which a treatment causes an outcome. However, in both clinical trials and in observational settings, interference can pose a threat to identification and estimation. In this work, we extend the notion of an exposure mapping to that of a generalized counterfactual mapping, in order to accommodate interference in both exposure-outcome and mediator-outcome relationships under a difference-in-differences design. After establishing identification results, we further develop multiply robust, semi-parametric efficient estimators of the controlled direct effect when counterfactual mappings are correctly specified. We then assess the small-sample performance of these estimators in various simulation settings. Finally, we apply these methods to estimate the controlled direct effect of the 2017 Philadelphia beverage tax on the volume sales of sweetened beverages, which may be mediated by neighborhood-level price heterogeneity.