In many stochastic service systems, decision-makers find themselves making a sequence of decisions, with the number of decisions being unpredictable. To enhance these decisions, it is crucial to uncover the causal impact these decisions have through careful analysis of observational data from the system. However, these decisions are not made independently, as they are shaped by previous decisions and outcomes. This phenomenon is called sequential bias and violates a key assumption in causal inference that one person's decision does not interfere with the potential outcomes of another. To address this issue, we establish a connection between sequential bias and the subfield of causal inference known as dynamic treatment regimes. We expand these frameworks to account for the random number of decisions by modeling the decision-making process as a marked point process. Consequently, we can define and identify causal effects to quantify sequential bias. Moreover, we propose estimators and explore their properties, including double robustness and semiparametric efficiency. In a case study of 27,831 encounters with a large academic emergency department, we use our approach to demonstrate that the decision to route a patient to an area for low acuity patients has a significant impact on the care of future patients.

翻译：在许多随机服务系统中，决策者需要做出一系列决策，且决策次数不可预测。为优化这些决策，关键在于通过系统观测数据深入分析决策的因果效应。然而，这些决策并非独立做出，而是受先前决策与结果的影响。这一现象被称为序贯偏差，它违背了因果推断中"某人的决策不干扰他人潜在结果"的核心假设。为解决该问题，我们建立了序贯偏差与因果推断中动态治疗方案子领域之间的联系。通过将决策过程建模为标记点过程，我们扩展了这些框架以容纳随机决策次数。由此，我们能够定义并识别量化序贯偏差的因果效应。此外，我们提出了估计量，并探索其双重稳健性与半参数效率等性质。在一项涉及大型学术急诊科27,831次就诊的案例研究中，我们运用该方法证明：将患者分流至低急症区域的决定对未来患者的照护具有显著影响。