Finding Regions of Heterogeneity in Decision-Making via Expected Conditional Covariance

TL;DR

This paper introduces an algorithm to identify regions where decision-makers differ significantly in their choices, using causal inference and empirical optimization, with applications demonstrated in healthcare data.

Contribution

The paper presents a novel algorithm for detecting regions of heterogeneity in decision-making, formalized as a causal inference problem, with theoretical guarantees and real-world validation.

Findings

Accurately recovers regions of decision heterogeneity in semi-synthetic data.

Aligns with clinical knowledge in healthcare datasets.

Provides generalization bounds for the proposed method.

Abstract

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.