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Research papers

How the scientific community uses the Chambers and their data.

Here you can find a 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.

Did we miss your paper? Has the preprint been published?

Let us know at contact@causalchamber.ai and we'll be happy to fix it!

By publication date

experiment design

Testing when adaptive data acquisition can replace fixed measurement plans

Jia Bi, Samuel Pinilla, Chenyang Zhu

arXiv preprint arXiv:2607.27651

causal inference

DAG-FM: A Foundation Model for Causal Discovery under Heterogeneous Causal Mechanisms

Yikang Chen, Zhengkang Guan, Haoyuan Qian, Peng Cui, Yi Yang, Kun Kuang

arXiv preprint arXiv:2607.11510

causal inference

CDFM: Towards a General-Purpose Causal Discovery Foundation Model

Jie Qiao, Ruichu Cai, Zijian Li, Weilin Chen, Pengfei Hua, Boyan Xu, Zhengming Chen, Zhifeng Hao, Peng Cui

arXiv preprint arXiv:2607.11508

causal inferenceICML

Many Experiments, Few Repetitions, Unpaired Data, and Sparse Effects: Is Causal Inference Possible?

Felix Schur, Niklas Pfister, Peng Ding, Sach Mukherjee, Jonas Peters

ICML 2026

causal inference

FoundCause: Causal Discovery with Latent Confounders from Observational Data

Patrick Blöbaum, Krishnakumar Balasubramanian, Shiva Prasad Kasiviswanathan

arXiv preprint arXiv:2606.17516

causal inference

Tensor-based second-order causal discovery

Nathan Ouyang, Kexin Wang, Anna Seigal

arXiv preprint arXiv:2606.18074

causal inferencedomain generalization

How Useful is Causal Invariance for Domain Adaptation in Finite-Sample Settings?

Julia Kostin, Kasra Jalaldoust, Elias Bareinboim, Samory Kpotufe, Fanny Yang

arXiv preprint arXiv:2606.12680

causal inference

EML-CD: Causal Mechanism Recovery via EML Symbolic Trees in Structure Learning

Sota Asanuma

arXiv preprint arXiv:2606.05942

SBIICML

Flow Matching Calibration for Simulation-Based Inference under Model Misspecification

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

ICML 2026

agentsLLMs

Harnessing Generalist Agents for Contextualized Time Series

Zihao Li, Kaifeng Jin, Yuanchen Bei, Jiaru Zou, Avaneesh Kumar, Xuying Ning, Yanjun Zhao, Mengting Ai, Baoyu Jing, Hanghang Tong, Jingrui He

arXiv preprint arXiv:2606.05404

causal inferenceroot cause analysis

ORCA: An End-to-End Interactive Copilot for Optimized Root Cause Analysis

Phi Nguyen Xuan, Nicholas Tagliapietra, Lavdim Halilaj, Kristian Kersting, Juergen Luettin

arXiv preprint arXiv:2605.27022

causal inference

Towards Continuous-time Causal Foundation Models

Dennis Thumm, Ruben Wiedemann, Ying Chen

arXiv preprint arXiv:2605.28880

causal inference

Prediction-Intervention Games and Invariant Sets

Linus Kühne, Felix Schur, Jonas Peters

arXiv preprint arXiv:2605.16828

reinforcement learningroboticsICML

Trajectory-Level Data Augmentation for Offline Reinforcement Learning

Tobias Schmähling, Matthias Burkhardt, Tobias Windisch

To appear in ICML

SBI

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

causal inference

Identifying Causal Effects Using a Single Proxy Variable

Silvan Vollmer, Niklas Pfister, Sebastian Weichwald

arXiv preprint arXiv:2604.09135

Read the case study.

causal 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)

causal inference

Nonparametric Greedy Equivalence Search with Prior-Fitted Networks

Mateusz Gajewski, Mateusz Olko

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

causal 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

hybrid models

Learning Deep Hybrid Models with Sharpness-Aware Minimization

Naoya Takeishi

arXiv preprint arXiv:2602.06837

causal inferenceanomaly detection

Causal Characterization of Measurement and Mechanistic Anomalies

Hendrik Suhr, David Kaltenpoth, Jilles Vreeken

arXiv preprint arXiv:2601.23026

causal inference

Coarsening Causal DAG Models

Francisco Madaleno, Pratik Misra, Alex Markham

arXiv preprint arXiv:2601.10531

SBINeurIPS

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)

causal 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

causal 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

causal inferenceNeurIPS

Flow-Based Non-stationary Temporal Regime Causal Structure Learning

Abdellah Rahmani, Pascal Frossard

Advances in Neural Information Processing Systems 38 (NeurIPS 2025)

density estimation

CINDES: Classification induced neural density estimator and simulator

Dehao Dai, Jianqing Fan, Yihong Gu, Debarghya Mukherjee

arXiv preprint arXiv:2510.00367

LLMs

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

causal 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

LLM 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

SBIICML

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)

causal 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)

domain generalizationJMLR

Invariant Subspace Decomposition

Margherita Lazzaretto, Jonas Peters, Niklas Pfister

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

causal inference

Algorithmic Statistical Learning and Causality Pursuit Using Neural Networks

Yihong Gu

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

causal 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

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