Conformal Risk-Averse Decision Making with Optimized Certainty Equivalent Risk Control

Amirmohammad Farzaneh; Osvaldo Simeone

stat.MLAug 28, 2026

Conformal Risk-Averse Decision Making with Optimized Certainty Equivalent Risk Control

Authors: Amirmohammad Farzaneh, Osvaldo Simeone

Organizations: Institute for Intelligent Networked Systems (INSI), Northeastern University London, London, UK

Abstract

We study risk-averse decision making, in which an agent selects actions while being uncertain about the true system state. The risk is measured via optimized certainty equivalent (OCE) metrics, which generalize popular criteria such as mean-variance risk and conditional value-at-risk (CVaR). We characterize the optimal policy under known distributions, and show that it reduces to a prediction set-based solution for the CVaR. This provides an operational interpretation of conformal prediction-type prediction sets. For unknown distributions, we develop a data-driven calibration strategy, based on a synthetic model for the likelihood and held-out calibration data, yielding high-probability control of the OCE risk. The approach is evaluated on two wireless beamforming settings.

Figures & tables

Panel of Figure 1 — Fig. 1 : Line-of-sight channel: (a) Optimized beam steering angle at nominal angle of departure $X=0$ ; and (b) $\CVaR_{0.30}$ as a function of the probability of a positive channel estimation error (equal to $+3^{\circ}$ ).

Fig. 2 : Ray-traced channel: Distribution of empirical

\CVaR_{0.20}

over 500 Monte Carlo redraws of the test channel realizations, with target loss

\varepsilon\!=\!0.25

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Conformal Risk-Averse Decision Making with Action Conditional Guarantee

Zihan Zhu, Shayan Kiyani, George Pappas, Hamed Hassani

University of Pennsylvania

Group-Conditional Conformal Prediction Conformal Prediction Decision-Making under Uncertainty

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Reliable decision making pipelines powered by machine learning models require uncertainty quantification (UQ) methods that come with explicit safety guarantees. Conformal prediction provides such UQ by wrapping ML predictions into prediction sets, and recent work by Kiyani et al. (2025b) established that these sets can be translated into optimal risk-averse decision policies -- yet only inheriting marginal safety guarantees. We generalize and strengthen their results by (i) introducing action-conditional conformal prediction, which yields safety guarantees conditioned explicitly on each action taken by the decision maker, (ii) showing that action-conditional prediction sets serve as a proxy for the feasible decision space for risk-averse decision makers aiming to optimize action-conditional value-at-risk, and (iii) proposing a principled finite-sample algorithm based on pinball-loss minimization, connecting the framework of Gibbs et al. (2025) to action-conditional guarantees. Experiments on two real-world datasets confirm that our approach significantly improves action-conditional performance over conformal baselines.

Abstract

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