Finding Regions of Heterogeneity in Decision-Making via Expected Conditional Covariance
This work addresses the need to pinpoint areas of inconsistency in decision-making for fields like law and healthcare, though it is incremental as it builds on existing causal inference methods.
The paper tackles the problem of identifying contexts where decision-makers exhibit high disagreement, formalizing it as a causal inference task and presenting an algorithm to find such regions. In semi-synthetic experiments, the algorithm accurately recovers correct regions compared to baselines, and in real-world healthcare datasets, it identifies variation consistent with clinical knowledge.
Individuals often make different decisions when faced with the same context, due to personal preferences and background. For instance, judges may vary in their leniency towards certain drug-related offenses, and doctors may vary in their preference for how to start treatment for certain types of patients. With these examples in mind, we present an algorithm for identifying types of contexts (e.g., types of cases or patients) with high inter-decision-maker disagreement. We formalize this as a causal inference problem, seeking a region where the assignment of decision-maker has a large causal effect on the decision. Our algorithm finds such a region by maximizing an empirical objective, and we give a generalization bound for its performance. In a semi-synthetic experiment, we show that our algorithm recovers the correct region of heterogeneity accurately compared to baselines. Finally, we apply our algorithm to real-world healthcare datasets, recovering variation that aligns with existing clinical knowledge.