Research papers

How the scientific community uses the Chambers and their data.

Here you can find a (non-exhaustive) selection of research papers that use the Chambers as a testbed to validate algorithms and methodology, either with data collected through the Remote Lab, or from datasets in our open-source repository.

For the complete list of papers citing the chambers, including those doing so as motivation or related work, please check the Google Scholar page.

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By publication date

May 7, 2026

chamber dataSBI

Information-Preserving Domain Transfer with Unlabeled Data in Misspecified Simulation-Based Inference

Joon Jang, Eunho Jeong, Kyu Sung Choi, Hyeonjin Kim

arXiv preprint arXiv:2605.05652

April 10, 2026

chamber datacausal inference

Identifying Causal Effects Using a Single Proxy Variable

Silvan Vollmer, Niklas Pfister, Sebastian Weichwald

arXiv preprint arXiv:2604.09135

Read the case study.

April 10, 2026

chamber datacausal inference

Causal generalized linear models via Pearson risk invariance

Alice Polinelli, Veronica Vinciotti, Ernst C. Wit

Journal of Causal Inference, Vol. 14, No. 1, Art. 20240043 (De Gruyter)

March 10, 2026

chamber datacausal inference

Nonparametric Greedy Equivalence Search with Prior-Fitted Networks

Mateusz Gajewski, Mateusz Olko

Proceedings of Machine Learning Research, Vol. 323, pp. 1–26

February 20, 2026

chamber datacausal inferencedomain generalizationICML

Anti-causal domain generalization: Leveraging unlabeled data

Sorawit Saengkyongam, Juan L. Gamella, Andrew C. Miller, Jonas Peters, Nicolai Meinshausen, Christina Heinze-Deml

arXiv preprint arXiv:2602.17187

February 6, 2026

chamber datahybrid models

Learning Deep Hybrid Models with Sharpness-Aware Minimization

Naoya Takeishi

arXiv preprint arXiv:2602.06837

January 30, 2026

chamber datacausal inferenceanomaly detection

Causal Characterization of Measurement and Mechanistic Anomalies

Hendrik Suhr, David Kaltenpoth, Jilles Vreeken

arXiv preprint arXiv:2601.23026

January 15, 2026

chamber datacausal inference

Coarsening Causal DAG Models

Francisco Madaleno, Pratik Misra, Alex Markham

arXiv preprint arXiv:2601.10531

December 5, 2025

chamber dataSBINeurIPS

Inductive Domain Transfer In Misspecified Simulation-Based Inference

Ortal Senouf, Antoine Wehenkel, Cédric Vincent-Cuaz, Emmanuel Abbé, Pascal Frossard

Advances in Neural Information Processing Systems 38 (NeurIPS 2025)

November 26, 2025

chamber datacausal inference

Convex Mixed-Integer Programming for Causal Additive Models with Optimization and Statistical Guarantees

Xiaozhu Zhang, Nir Keret, Ali Shojaie, Armeen Taeb

arXiv preprint arXiv:2511.21126

November 13, 2025

chamber datacausal inference

Causality Pursuit from Heterogeneous Environments via Neural Adversarial Invariance Learning

Yihong Gu, Cong Fang, Peter Bühlmann, Jianqing Fan

The Annals of Statistics, Vol. 53, No. 5, pp. 2230–2257

October 23, 2025

chamber datacausal inferenceNeurIPS

Flow-Based Non-stationary Temporal Regime Causal Structure Learning

Abdellah Rahmani, Pascal Frossard

Advances in Neural Information Processing Systems 38 (NeurIPS 2025)

October 1, 2025

chamber datadensity estimation

CINDES: Classification induced neural density estimator and simulator

Dehao Dai, Jianqing Fan, Yihong Gu, Debarghya Mukherjee

arXiv preprint arXiv:2510.00367

September 27, 2025

chamber dataSBI

Flow Matching for Robust Simulation-Based Inference under Model Misspecification

Pierre-Louis Ruhlmann, Pedro L. C. Rodrigues, Michael Arbel, Florence Forbes

arXiv preprint arXiv:2509.23385

August 13, 2025

chamber dataLLMs

Beyond Naïve Prompting: Strategies for Improved Zero-shot Context-aided Forecasting with LLMs

Arjun Ashok, Andrew Robert Williams, Vincent Zhihao Zheng, Irina Rish, Nicolas Chapados, Étienne Marcotte, Valentina Zantedeschi, Alexandre Drouin

arXiv preprint arXiv:2508.09904

July 21, 2025

chamber datacausal inferenceBayesian optimization

Towards MFACBO: Multi-Fidelity Abstraction Causal Bayesian Optimization in the Context of the Abstraction-Fidelity Connection

Jakob Zeitler

1st Workshop on Causal Abstractions and Representations (CAR), UAI 2025

July 13, 2025

chamber dataLLM benchmarkICML

Context is Key: A Benchmark for Forecasting with Essential Textual Information

Andrew Robert Williams, Arjun Ashok, Étienne Marcotte, Valentina Zantedeschi, Jithendaraa Subramanian, Roland Riachi, James Requeima, Alexandre Lacoste, Irina Rish, Nicolas Chapados, Alexandre Drouin

Proceedings of the 42nd International Conference on Machine Learning (ICML 2025), PMLR 267, pp. 66887–66944

July 13, 2025

chamber dataSBIICML

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

Antoine Wehenkel, Juan L. Gamella, Ozan Sener, Jens Behrmann, Guillermo Sapiro, Jörn-Henrik Jacobsen, Marco Cuturi

Proceedings of the 42nd International Conference on Machine Learning (ICML 2025), PMLR 267 (oral presentation)

July 13, 2025

chamber datacausal inferenceICML

Sanity Checking Causal Representation Learning on a Simple Real-World System

Juan L. Gamella, Simon Bing, Jakob Runge

Proceedings of the 42nd International Conference on Machine Learning (ICML 2025) (oral presentation)

June 22, 2025

chamber datadomain generalizationJMLR

Invariant Subspace Decomposition

Margherita Lazzaretto, Jonas Peters, Niklas Pfister

Journal of Machine Learning Research, Vol. 26, No. 95, pp. 1–56

May 1, 2025

chamber datacausal inference

Algorithmic Statistical Learning and Causality Pursuit Using Neural Networks

Yihong Gu

PhD Thesis, Princeton University (Department of Operations Research and Financial Engineering)

March 12, 2025

chamber datacausal inference

Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventions

Juan L. Gamella, Armeen Taeb, Christina Heinze-Deml, Peter Bühlmann

arXiv preprint arXiv:2211.14897

PreviousEffect estimation with proxies

Last updated 6 days ago

Good afternoon

hashtagBy publication date

hashtagInformation-Preserving Domain Transfer with Unlabeled Data in Misspecified Simulation-Based Inference

hashtagIdentifying Causal Effects Using a Single Proxy Variable

hashtagCausal generalized linear models via Pearson risk invariance

hashtagNonparametric Greedy Equivalence Search with Prior-Fitted Networks

hashtagAnti-causal domain generalization: Leveraging unlabeled data

hashtagLearning Deep Hybrid Models with Sharpness-Aware Minimization

hashtagCausal Characterization of Measurement and Mechanistic Anomalies

hashtagCoarsening Causal DAG Models

hashtagInductive Domain Transfer In Misspecified Simulation-Based Inference

hashtagConvex Mixed-Integer Programming for Causal Additive Models with Optimization and Statistical Guarantees

hashtagCausality Pursuit from Heterogeneous Environments via Neural Adversarial Invariance Learning

hashtagFlow-Based Non-stationary Temporal Regime Causal Structure Learning

hashtagCINDES: Classification induced neural density estimator and simulator

hashtagFlow Matching for Robust Simulation-Based Inference under Model Misspecification

hashtagBeyond Naïve Prompting: Strategies for Improved Zero-shot Context-aided Forecasting with LLMs

hashtagTowards MFACBO: Multi-Fidelity Abstraction Causal Bayesian Optimization in the Context of the Abstraction-Fidelity Connection

hashtagContext is Key: A Benchmark for Forecasting with Essential Textual Information

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

hashtagSanity Checking Causal Representation Learning on a Simple Real-World System

hashtagInvariant Subspace Decomposition

hashtagAlgorithmic Statistical Learning and Causality Pursuit Using Neural Networks

hashtagCharacterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventions

By publication date

Information-Preserving Domain Transfer with Unlabeled Data in Misspecified Simulation-Based Inference

Identifying Causal Effects Using a Single Proxy Variable

Causal generalized linear models via Pearson risk invariance

Nonparametric Greedy Equivalence Search with Prior-Fitted Networks

Anti-causal domain generalization: Leveraging unlabeled data

Learning Deep Hybrid Models with Sharpness-Aware Minimization

Causal Characterization of Measurement and Mechanistic Anomalies

Coarsening Causal DAG Models

Inductive Domain Transfer In Misspecified Simulation-Based Inference

Convex Mixed-Integer Programming for Causal Additive Models with Optimization and Statistical Guarantees

Causality Pursuit from Heterogeneous Environments via Neural Adversarial Invariance Learning

Flow-Based Non-stationary Temporal Regime Causal Structure Learning

CINDES: Classification induced neural density estimator and simulator

Flow Matching for Robust Simulation-Based Inference under Model Misspecification

Beyond Naïve Prompting: Strategies for Improved Zero-shot Context-aided Forecasting with LLMs

Towards MFACBO: Multi-Fidelity Abstraction Causal Bayesian Optimization in the Context of the Abstraction-Fidelity Connection

Context is Key: A Benchmark for Forecasting with Essential Textual Information

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

Sanity Checking Causal Representation Learning on a Simple Real-World System

Invariant Subspace Decomposition

Algorithmic Statistical Learning and Causality Pursuit Using Neural Networks

Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventions