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Restless multi-arm bandits (RMABs) is a popular decision-theoretic frameworkthat has been used to model real-world sequential decision making problems inpublic health, wildlife conservation, communication systems, and beyond.Deployed RMAB systems typically operate in two stages: the first predicts theunknown parameters defining the RMAB instance, and the second employs anoptimization algorithm to solve the constructed RMAB instance.  In this work we provide and analyze the results from a first-of-its-kinddeployment of an RMAB system in public health domain, aimed at improvingmaternal and child health. Our analysis is focused towards understanding therelationship between prediction accuracy and overall performance of deployedRMAB systems. This is crucial for determining the value of investing inimproving predictive accuracy towards improving the final system performance,and is useful for diagnosing, monitoring deployed RMAB systems.  Using real-world data from our deployed RMAB system, we demonstrate that animprovement in overall prediction accuracy may even be accompanied by adegradation in the performance of RMAB system -- a broad investment ofresources to improve overall prediction accuracy may not yield expectedresults. Following this, we develop decision-focused evaluation metrics toevaluate the predictive component and show that it is better at explaining(both empirically and theoretically) the overall performance of a deployed RMABsystem.

Decision-Focused Evaluation: Analyzing Performance of Deployed Restless Multi-Arm Bandits