Incorporating nonparametric methods for estimating causal excursion effects in mobile health with zero-inflated count outcomes

TL;DR

This paper develops nonparametric estimation methods for causal excursion effects in mobile health trials with zero-inflated count outcomes, addressing a gap in analyzing such data types.

Contribution

It introduces novel nonparametric estimators for causal excursion effects specifically tailored for zero-inflated count data in mobile health studies.

Findings

Proposed estimators perform well in simulations.

Applied methods successfully to Drink Less trial data.

Established bidirectional asymptotics for the estimators.

Abstract

In mobile health, tailoring interventions for real-time delivery is of paramount importance. Micro-randomized trials have emerged as the "gold-standard" methodology for developing such interventions. Analyzing data from these trials provides insights into the efficacy of interventions and the potential moderation by specific covariates. The "causal excursion effect", a novel class of causal estimand, addresses these inquiries. Yet, existing research mainly focuses on continuous or binary data, leaving count data largely unexplored. The current work is motivated by the Drink Less micro-randomized trial from the UK, which focuses on a zero-inflated proximal outcome, i.e., the number of screen views in the subsequent hour following the intervention decision point. To be specific, we revisit the concept of causal excursion effect, specifically for zero-inflated count outcomes, and introduce novel estimation approaches that incorporate nonparametric techniques. Bidirectional asymptotics are established for the proposed estimators. Simulation studies are conducted to evaluate the performance of the proposed methods. As an illustration, we also implement these methods to the Drink Less trial data.