Empowering AI research with physical testbeds
The Causal Chambers are a playground for AI agents and algorithms that interact with the physical world. Each chamber is a miniature laboratory that provides a digital interface to well-understood physical phenomena. This allows researchers to test their algorithms in a controlled but non-simulated environment, yielding insights into why they fail and how to improve them.
If you would like to learn more about the chambers, or use them for your own research or teaching, please get in touch below. We are happy to help!
Access the Chambers
The chambers are a tool for educators and scientists who do basic research in AI & ML. Depending on your needs, there are several ways you can access the chambers and their data.
Dataset repository
Fully documented, open-source datasets collected from the chambers. Updated regularly with new experiments & benchmarks.
Please reach out if you need help navigating the repository.
Custom Datasets
Do you have a use case in mind but the appropriate dataset is not yet on the repository?
We can collect custom datasets on request. We are also happy to help you design new benchmarks and collaborate on scientific research.
Own a Chamber
Collect your own datasets with full, uninterrupted access to the chambers. For applications in active learning, RL & control, etc. For conference competitions, teaching and demonstrations.
Manufactured in Switzerland
1-year warranty
Full documentation
Set-up support
Research
Research papers that use chamber data
Causal chambers as a real-world physical testbed for AI methodology
Juan L. Gamella, Jonas Peters and Peter Bühlmann
Context is Key: A Benchmark for Forecasting with Essential Textual Information
Arjun Ashok, Andrew Robert Williams, Étienne Marcotte, Valentina Zantedeschi, Jithendaraa Subramanian, Roland Riachi, James Requeima, Alexandre Lacoste, Irina Rish, Nicolas Chapados, Alexandre Drouin
An Asymptotically Optimal Coordinate Descent Algorithm for Learning Bayesian Networks from Gaussian Models
Tong Xu, Armeen Taeb, Simge Kuccukyavuz, Ali Shojaie
The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applications
Philippe Brouillard, Chandler Squires, Jonas Wahl, Konrad P. Kording, Karen Sachs, Alexandre Drouin, Dhanya Sridhar
Sortability of Time Series Data
Christopher Lohse and Jonas Wahl
CI4TS Workshop @ UAI 2024
Causality Pursuit from Heterogeneous Environments via Neural Adversarial Invariance Learning
Yihong Gu, Cong Fang, Peter Bühlmann and Jianqing Fan
Addressing Misspecification in Simulation-based Inference through Data-driven Calibration
Antoine Wehenkel, Juan L. Gamella, Ozan Sener, Jens Behrmann, Guillermo Sapiro, Marco Cuturi and Jörn-Henrik Jacobsen
Invariant Subspace Decomposition
Margherita Lazzaretto, Jonas Peters, Niklas Pfister
arXiv preprint arXiv:2404.09962
Sanity Checking Causal Representation Learning on a Simple Real-World System
Juan L. Gamella*, Simon Bing, Jakob Runge
Forty-second International Conference on Machine Learning (ICML) 2025
Flow-Based Non-stationary Temporal Regime Causal Structure Learning
Abdellah Rahmani, Pascal Frossard
arXiv preprint arXiv:2506.17065