Inverse Contextual Bandits: Learning How Behavior Evolves over Time

TL;DR

This paper introduces Inverse Contextual Bandits, a method to learn and interpret how decision-making policies evolve over time, especially in non-stationary settings like healthcare, using offline data and formal modeling.

Contribution

It formalizes the problem of learning evolving decision policies as Inverse Contextual Bandits and proposes two algorithms for interpretable, non-stationary policy learning.

Findings

Effective in modeling evolving behavior in healthcare data

Provides interpretable representations of policy changes

Validated on real and simulated liver transplantation data

Abstract

Understanding a decision-maker's priorities by observing their behavior is critical for transparency and accountability in decision processes, such as in healthcare. Though conventional approaches to policy learning almost invariably assume stationarity in behavior, this is hardly true in practice: Medical practice is constantly evolving as clinical professionals fine-tune their knowledge over time. For instance, as the medical community's understanding of organ transplantations has progressed over the years, a pertinent question is: How have actual organ allocation policies been evolving? To give an answer, we desire a policy learning method that provides interpretable representations of decision-making, in particular capturing an agent's non-stationary knowledge of the world, as well as operating in an offline manner. First, we model the evolving behavior of decision-makers in terms of contextual bandits, and formalize the problem of Inverse Contextual Bandits (ICB). Second, we propose two concrete algorithms as solutions, learning parametric and nonparametric representations of an agent's behavior. Finally, using both real and simulated data for liver transplantations, we illustrate the applicability and explainability of our method, as well as benchmarking and validating its accuracy.