Unveiling Bias in Sequential Decision Making: A Causal Inference Approach for Stochastic Service Systems
揭示序贯决策偏差：随机服务系统的因果推断方法AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要研究序贯决策的因果效应，并明确涉及前序患者决策对后续患者潜在结果的干扰。
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揭示序贯决策偏差：随机服务系统的因果推断方法
- 研究问题
- 在决策次数随机且前序决策影响后续患者结果的服务系统中，如何定义和识别因果效应，并量化序贯决策偏差？
- 方法与思路
- 作者将序贯偏差与动态治疗方案框架相联系，用带标记点过程建模随机数量的决策，并提出估计量、研究双重稳健性与效率。
- 材料陈述的贡献
- 摘要称方法能够定义并识别序贯偏差相关效应；急诊案例显示，将患者分流至低病情区域的决策会显著影响后续患者照护。
- 阅读关注点
- 应核对点过程中的干预定义、历史信息和识别假设，以及跨患者影响如何进入目标量、双重稳健性与效率结论。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未获核实；摘要未说明识别条件、影响方向和效应大小，不能外推急诊案例的因果结论。
原文摘要
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.