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Finding Regions of Heterogeneity in Decision-Making via Expected Conditional Covariance
Justin Lim · Christina Ji · Michael Oberst · Saul Blecker · Leora Horwitz · David Sontag

Tue Dec 07 08:30 AM -- 10:00 AM (PST) @

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.

Author Information

Justin Lim (Massachusetts Institute of Technology)
Christina Ji (Massachusetts Institute of Technology)
Michael Oberst (MIT)
Saul Blecker (NYU Langone)
Leora Horwitz (NYU Langone)
David Sontag (MIT)

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