Efficient and Globally Robust Causal Excursion Effect Estimation

TL;DR

This paper develops a semiparametric efficient estimator for causal excursion effects in micro-randomized trials, offering robustness to model misspecification and demonstrating improved efficiency through simulations and real data.

Contribution

It introduces a class of two-stage estimators that achieve the efficiency bound and are robust to nuisance model misspecification, with a general theory for globally robust Z-estimators.

Findings

Proposed estimators attain the semiparametric efficiency bound.

Simulation studies show substantial efficiency gains over existing methods.

Application to Drink Less trial demonstrates practical effectiveness.

Abstract

Causal excursion effect (CEE) characterizes the effect of an intervention under policies that deviate from the experimental policy. It is widely used to study the effect of time-varying interventions that have the potential to be frequently adaptive, such as those delivered through smartphones. We study the semiparametric efficient estimation of CEE and we derive a semiparametric efficiency bound for CEE with identity or log link functions under working assumptions, in the context of micro-randomized trials. We propose a class of two-stage estimators that achieve the efficiency bound and are robust to misspecified nuisance models. In deriving the asymptotic property of the estimators, we establish a general theory for globally robust Z-estimators with either cross-fitted or non-cross-fitted nuisance parameters. We demonstrate substantial efficiency gain of the proposed estimator compared to existing ones through simulations and a real data application using the Drink Less micro-randomized trial.