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，针对实时投放的个性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。分析这些试验数据可深入了解干预措施的有效性及特定协变量的潜在调节作用。"因果偏移效应"作为一类新型因果推断量，正是用于解决这些问题。然而，现有研究主要关注连续型或二值数据，对计数数据的探索尚不充分。本研究受英国"少饮酒"微随机试验启发，该试验关注零膨胀的近期结局指标，即干预决策点后一小时内屏幕观看次数。具体而言，我们重新审视了专为零膨胀计数结果设计的因果偏移效应概念，并提出了融合非参数技术的新型估计方法。为所提出的估计量建立了双向渐近性质，通过模拟研究评估了新方法的性能，并应用这些方法对"少饮酒"试验数据进行了实证分析。