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We introduce Conformal Decision Theory, a framework for producing safeautonomous decisions despite imperfect machine learning predictions. Examplesof such decisions are ubiquitous, from robot planning algorithms that rely onpedestrian predictions, to calibrating autonomous manufacturing to exhibit highthroughput and low error, to the choice of trusting a nominal policy versusswitching to a safe backup policy at run-time. The decisions produced by ouralgorithms are safe in the sense that they come with provable statisticalguarantees of having low risk without any assumptions on the world modelwhatsoever; the observations need not be I.I.D. and can even be adversarial.The theory extends results from conformal prediction to calibrate decisionsdirectly, without requiring the construction of prediction sets. Experimentsdemonstrate the utility of our approach in robot motion planning around humans,automated stock trading, and robot manufacturing.

Conformal Decision Theory: Safe Autonomous Decisions from Imperfect Predictions