Observational Counterfactual Explanations in Sequential Decision Making

Human decision-making is plagued by a considerable amount of noise, as evidenced by the research of (Kahneman et al., 2021), which revealed thatmedical doctors in the same hospital often arrive at different treatment decisions when presented with identical patient cases. In this work, we utilize observational (backtracking) counterfactual explanations to support the diagnostic processin sequential decision-making. In particular, we explore how this approach can be effectively employed when decisions are influenced solely by external factors like the decision maker’s fatigue levels, or personal biases. We aim to strengthen trust in the decision-making process by exploring these counterfactual explanations.