CP4SBI: Local Conformal Calibration of Credible Sets in Simulation-Based Inference

• URL: http://arxiv.org/abs/2508.17077v2
• Date: Wed, 27 Aug 2025 13:24:35 GMT
• Title: CP4SBI: Local Conformal Calibration of Credible Sets in Simulation-Based Inference
• Abstract summary: We develop a model-agnostic conformal calibration framework that constructs credible sets with local Bayesian coverage.<n>Our two proposed variants, namely local calibration via regression trees and CDF-based calibration, enable finite-sample local coverage guarantees for any scoring function.
• Score: 1.8840155706520811
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Current experimental scientists have been increasingly relying on simulation-based inference (SBI) to invert complex non-linear models with intractable likelihoods. However, posterior approximations obtained with SBI are often miscalibrated, causing credible regions to undercover true parameters. We develop $\texttt{CP4SBI}$, a model-agnostic conformal calibration framework that constructs credible sets with local Bayesian coverage. Our two proposed variants, namely local calibration via regression trees and CDF-based calibration, enable finite-sample local coverage guarantees for any scoring function, including HPD, symmetric, and quantile-based regions. Experiments on widely used SBI benchmarks demonstrate that our approach improves the quality of uncertainty quantification for neural posterior estimators using both normalizing flows and score-diffusion modeling.

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