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In many stochastic service systems, decision-makers find themselves making asequence of decisions, with the number of decisions being unpredictable. Toenhance these decisions, it is crucial to uncover the causal impact thesedecisions have through careful analysis of observational data from the system.However, these decisions are not made independently, as they are shaped byprevious decisions and outcomes. This phenomenon is called sequential bias andviolates a key assumption in causal inference that one person's decision doesnot interfere with the potential outcomes of another. To address this issue, weestablish a connection between sequential bias and the subfield of causalinference known as dynamic treatment regimes. We expand these frameworks toaccount for the random number of decisions by modeling the decision-makingprocess as a marked point process. Consequently, we can define and identifycausal effects to quantify sequential bias. Moreover, we propose estimators andexplore their properties, including double robustness and semiparametricefficiency. In a case study of 27,831 encounters with a large academicemergency department, we use our approach to demonstrate that the decision toroute a patient to an area for low acuity patients has a significant impact onthe care of future patients.

Unveiling Bias in Sequential Decision Making: A Causal Inference Approach for Stochastic Service Systems