Nonparametric causal mediation analysis for stochastic interventional (in)direct effects

TL;DR

This paper develops a flexible, nonparametric framework for causal mediation analysis that handles stochastic interventions, continuous treatments, and intermediate confounders, enabling more robust and applicable causal effect estimation.

Contribution

It introduces a novel nonparametric approach for identifying and estimating stochastic interventional (in)direct effects without restrictive assumptions on treatment types or confounders.

Findings

Effects are identifiable without cross-world independence assumptions.

Provides efficient, multiply robust estimators for the effects.

Includes software for practical implementation.

Abstract

Causal mediation analysis has historically been limited in two important ways: (i) a focus has traditionally been placed on binary treatments and static interventions, and (ii) direct and indirect effect decompositions have been pursued that are only identifiable in the absence of intermediate confounders affected by treatment. We present a theoretical study of an (in)direct effect decomposition of the population intervention effect, defined by stochastic interventions jointly applied to the treatment and mediators. In contrast to existing proposals, our causal effects can be evaluated regardless of whether a treatment is categorical or continuous and remain well-defined even in the presence of intermediate confounders affected by treatment. Our (in)direct effects are identifiable without a restrictive assumption on cross-world counterfactual independencies, allowing for substantive conclusions drawn from them to be validated in randomized controlled trials. Beyond the novel effects introduced, we provide a careful study of nonparametric efficiency theory relevant for the construction of flexible, multiply robust estimators of our (in)direct effects, while avoiding undue restrictions induced by assuming parametric models of nuisance parameter functionals. To complement our nonparametric estimation strategy, we introduce inferential techniques for constructing confidence intervals and hypothesis tests, and discuss open source software implementing the proposed methodology.