In the domain of 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, backed by current semiparametric inference techniques. Yet, existing methods mainly focus 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, the number of screen views in the subsequent hour following the intervention decision point. In the current paper, we revisit the concept of causal excursion effects, specifically for zero-inflated count outcomes, and introduce novel estimation approaches that incorporate nonparametric techniques. Bidirectional asymptotics are derived for the proposed estimators. Through extensive simulation studies, we evaluate the performance of the proposed estimators. As an illustration, we also employ the proposed methods to the Drink Less trial data.

翻译：在移动健康领域，实时定制干预措施至关重要。微量随机试验已成为开发此类干预措施的“金标准”方法。分析这些试验的数据，可以深入了解干预措施的效果以及特定协变量可能存在的调节作用。“因果偏离效应”作为一类新型因果估计量，在现有半参数推断技术的支持下，回答了上述问题。然而，现有方法主要针对连续或二元数据，对计数数据的探索尚不充分。本研究源自英国“少饮酒”微量随机试验，该试验关注零膨胀近端结果——干预决策点后下一小时的屏幕观看次数。在本文中，我们重新审视了因果偏离效应的概念，特别针对零膨胀计数结果，并引入了结合非参数技术的新估计方法。我们推导了所提估计量的双向渐近性质。通过广泛的模拟研究，我们评估了所提估计量的性能。作为示例，我们还将其应用于“少饮酒”试验数据。