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.

翻译：在移动健康领域，实时定制化干预措施至关重要。微随机试验已成为开发此类干预措施的“金标准”方法论。分析这些试验数据能揭示干预措施的有效性以及特定协变量的潜在调节作用。“因果变动效应”作为一类新型因果估计量，正是为了解答这些问题而提出。然而，现有研究主要聚焦于连续或二元数据，对计数数据的探索尚显不足。本研究受英国Drink Less微随机试验启发，该试验关注零膨胀的近端结果变量，即干预决策点后一小时内屏幕浏览次数。具体而言，我们重新审视了因果变动效应的概念，特别针对零膨胀计数结果，引入了融合非参数技术的新型估计方法。针对所提出的估计量，我们建立了双向渐近性质。通过模拟研究评估了所提方法的性能，并以Drink Less试验数据为例进行了实际应用。