Addressing Misspecification in Simulation-based Inference through Data-driven Calibration

Antoine Wehenkel, Juan L. Gamella, Ozan Sener, Jens Behrmann, Guillermo Sapiro, Joern-Henrik Jacobsen, Marco Cuturi
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:65949-65980, 2025.

Abstract

Driven by steady progress in deep generative modeling, simulation-based inference (SBI) has emerged as the workhorse for inferring the parameters of stochastic simulators. However, recent work has demonstrated that model misspecification can harm SBI’s reliability, preventing its adoption in important applications where only misspecified simulators are available. This work introduces robust posterior estimation (RoPE), a framework that overcomes model misspecification with a small real-world calibration set of ground truth parameter measurements. We formalize the misspecification gap as the solution of an optimal transport (OT) problem between learned representations of real-world and simulated observations, allowing RoPE to learn a model of the misspecification without placing additional assumptions on its nature. RoPE shows how the calibration set and OT together offer a controllable balance between calibrated uncertainty and informative inference even under severely misspecified simulators. Results on four synthetic tasks and two real-world problems with ground-truth labels demonstrate that RoPE outperforms baselines and consistently returns informative and calibrated credible intervals.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-wehenkel25a, title = {Addressing Misspecification in Simulation-based Inference through Data-driven Calibration}, author = {Wehenkel, Antoine and Gamella, Juan L. and Sener, Ozan and Behrmann, Jens and Sapiro, Guillermo and Jacobsen, Joern-Henrik and Cuturi, Marco}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {65949--65980}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/wehenkel25a/wehenkel25a.pdf}, url = {https://proceedings.mlr.press/v267/wehenkel25a.html}, abstract = {Driven by steady progress in deep generative modeling, simulation-based inference (SBI) has emerged as the workhorse for inferring the parameters of stochastic simulators. However, recent work has demonstrated that model misspecification can harm SBI’s reliability, preventing its adoption in important applications where only misspecified simulators are available. This work introduces robust posterior estimation (RoPE), a framework that overcomes model misspecification with a small real-world calibration set of ground truth parameter measurements. We formalize the misspecification gap as the solution of an optimal transport (OT) problem between learned representations of real-world and simulated observations, allowing RoPE to learn a model of the misspecification without placing additional assumptions on its nature. RoPE shows how the calibration set and OT together offer a controllable balance between calibrated uncertainty and informative inference even under severely misspecified simulators. Results on four synthetic tasks and two real-world problems with ground-truth labels demonstrate that RoPE outperforms baselines and consistently returns informative and calibrated credible intervals.} }
Endnote
%0 Conference Paper %T Addressing Misspecification in Simulation-based Inference through Data-driven Calibration %A Antoine Wehenkel %A Juan L. Gamella %A Ozan Sener %A Jens Behrmann %A Guillermo Sapiro %A Joern-Henrik Jacobsen %A Marco Cuturi %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-wehenkel25a %I PMLR %P 65949--65980 %U https://proceedings.mlr.press/v267/wehenkel25a.html %V 267 %X Driven by steady progress in deep generative modeling, simulation-based inference (SBI) has emerged as the workhorse for inferring the parameters of stochastic simulators. However, recent work has demonstrated that model misspecification can harm SBI’s reliability, preventing its adoption in important applications where only misspecified simulators are available. This work introduces robust posterior estimation (RoPE), a framework that overcomes model misspecification with a small real-world calibration set of ground truth parameter measurements. We formalize the misspecification gap as the solution of an optimal transport (OT) problem between learned representations of real-world and simulated observations, allowing RoPE to learn a model of the misspecification without placing additional assumptions on its nature. RoPE shows how the calibration set and OT together offer a controllable balance between calibrated uncertainty and informative inference even under severely misspecified simulators. Results on four synthetic tasks and two real-world problems with ground-truth labels demonstrate that RoPE outperforms baselines and consistently returns informative and calibrated credible intervals.
APA
Wehenkel, A., Gamella, J.L., Sener, O., Behrmann, J., Sapiro, G., Jacobsen, J. & Cuturi, M.. (2025). Addressing Misspecification in Simulation-based Inference through Data-driven Calibration. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:65949-65980 Available from https://proceedings.mlr.press/v267/wehenkel25a.html.

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