Non-parametric efficient causal mediation with intermediate confounders

Abstract

Interventional effects for mediation analysis were proposed as a solution to the lack of identifiability of natural (in)direct effects in the presence of a mediator-outcome confounder affected by exposure. We present a theoretical and computational study of the properties of the interventional (in)direct effect estimands based on the efficient influence fucntion (EIF) in the non-parametric statistical model. We use the EIF to develop two asymptotically optimal, non-parametric estimators that leverage data-adaptive regression for estimation of the nuisance parameters: a one-step estimator and a targeted minimum loss estimator. A free and open source \texttt{R} package implementing our proposed estimators is made available on GitHub. We further present results establishing the conditions under which these estimators are consistent, multiply robust, $n^{1/2}$-consistent and efficient. We illustrate the finite-sample performance of the estimators and corroborate our theoretical results in a simulation study. We also demonstrate the use of the estimators in our motivating application to elucidate the mechanisms behind the unintended harmful effects that a housing intervention had on adolescent girls' risk behavior.

References in corpus (6)

• Simultaneous analysis of Lasso and Dantzig selector
• Adaptive Concentration of Regression Trees, with Application to Random Forests
• Direct and Indirect Effects of Sequential Treatments
• Causal organic indirect and direct effects: closer to Baron and Kenny, with a product method for binary mediators
• On Partial Identification of the Pure Direct Effect
• Robust variance estimation and inference for causal effect estimation

Cited by in corpus (5)

• Non-linear Mediation Analysis with High-dimensional Mediators whose Causal Structure is Unknown
• The causal effects of modified treatment policies under network interference
• When the ends don't justify the means: Learning a treatment strategy to prevent harmful indirect effects
• Path-Free Decomposition for Direct, Indirect and Interaction Effects in Mediation Analysis
• Nonparametric causal mediation analysis for stochastic interventional (in)direct effects