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Individuals often make different decisions when faced with the same context,due to personal preferences and background. For instance, judges may vary intheir leniency towards certain drug-related offenses, and doctors may vary intheir preference for how to start treatment for certain types of patients. Withthese examples in mind, we present an algorithm for identifying types ofcontexts (e.g., types of cases or patients) with high inter-decision-makerdisagreement. We formalize this as a causal inference problem, seeking a regionwhere the assignment of decision-maker has a large causal effect on thedecision. Our algorithm finds such a region by maximizing an empiricalobjective, and we give a generalization bound for its performance. In asemi-synthetic experiment, we show that our algorithm recovers the correctregion of heterogeneity accurately compared to baselines. Finally, we apply ouralgorithm to real-world healthcare datasets, recovering variation that alignswith existing clinical knowledge.

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