Learning Pareto-Efficient Decisions with Confidence

TL;DR

This paper introduces a method for learning Pareto-efficient decisions under uncertainty, providing statistically confident trade-offs suitable for safety-critical applications, and adapts to limited context overlap.

Contribution

It extends Pareto efficiency to uncertain outcomes, integrating conformal prediction for confident decision-making in varying contexts.

Findings

Method achieves statistical guarantees on synthetic data.

Approach adapts to weak or no context overlap.

Evaluations demonstrate effectiveness on real data.

Abstract

The paper considers the problem of multi-objective decision support when outcomes are uncertain. We extend the concept of Pareto-efficient decisions to take into account the uncertainty of decision outcomes across varying contexts. This enables quantifying trade-offs between decisions in terms of tail outcomes that are relevant in safety-critical applications. We propose a method for learning efficient decisions with statistical confidence, building on results from the conformal prediction literature. The method adapts to weak or nonexistent context covariate overlap and its statistical guarantees are evaluated using both synthetic and real data.