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In the domain of mobile health, tailoring interventions for real-timedelivery is of paramount importance. Micro-randomized trials have emerged asthe "gold-standard" methodology for developing such interventions. Analyzingdata from these trials provides insights into the efficacy of interventions andthe potential moderation by specific covariates. The "causal excursion effect",a novel class of causal estimand, addresses these inquiries, backed by currentsemiparametric inference techniques. Yet, existing methods mainly focus oncontinuous or binary data, leaving count data largely unexplored. The currentwork is motivated by the Drink Less micro-randomized trial from the UK, whichfocuses on a zero-inflated proximal outcome, the number of screen views in thesubsequent hour following the intervention decision point. In the currentpaper, we revisit the concept of causal excursion effects, specifically forzero-inflated count outcomes, and introduce novel estimation approaches thatincorporate nonparametric techniques. Bidirectional asymptotics are derived forthe proposed estimators. Through extensive simulation studies, we evaluate theperformance of the proposed estimators. As an illustration, we also employ theproposed methods to the Drink Less trial data.

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