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

In many stochastic service systems, decision makers make 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, human decisions are not made in isolation; each one is influenced by the decisions and outcomes that precede it. This phenomenon is called sequential human bias and violates a key assumption in causal inference that one decision does not interfere with the potential outcomes of another. We address this by considering a connection between sequential human 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, called lag effects, to quantify sequential human bias. We propose estimators and explore their properties, including double robustness and semiparametric efficiency. In a study of 27,831 encounters with a large emergency department, we use our approach to demonstrate that the decision to route a patient to a vertical area has a significant impact on the care of future patients. Supplemental Material: The online appendices are available at https://doi.org/10.1287/stsy.2023.0034 .