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The study aims to analyze how sequential human bias affects decision-making in stochastic service systems using causal inference.

May 21, 2026Stochastic SystemsOpen Access

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

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Randomized trial uncovers causal impacts of sequential decisions in emergency care, suggesting improved patient routing strategies.

The study aims to analyze how sequential human bias affects decision-making in stochastic service systems using causal inference.
Analyzed observational data from a large emergency department (N=27,831 encounters).
Modeled decision-making as a marked point process to account for the random number of decisions.
Defined and identified lag effects to quantify sequential human bias.
Significant lag effects were observed, showing that routing decisions impact the care of future patients.
Proposed estimators demonstrated properties of double robustness and semiparametric efficiency.

Cochran et al. (2026) studied this question.

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