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

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.

Publication
Proceedings of the 35th Conference on Neural Information Processing Systems
Justin Lim
Justin Lim
Master’s student

Citadel

Christina X Ji
Christina X Ji
PhD Student

Christina is interested in applying machine learning to healthcare, detecting distribution shift and developing transfer learning algorithms, and evaluating treatments and reinforcement learning policies with causal inference.

Michael Oberst
Michael Oberst
PhD Student

Postdoc CMU, Incoming Asst Prof Johns Hopkins

David Sontag
David Sontag
Professor of EECS

My research focuses on advancing machine learning and artificial intelligence, and using these to transform health care.

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