Causal Chamber: Dataset Repository

This repository contains datasets collected from the causal chambers, the two devices described in the 2025 paper Causal chambers as a real-world physical testbed for AI methodology by Juan L. Gamella, Jonas Peters and Peter Bühlmann. The repository is updated as we collect new datasets from the chambers.

Important

You can run real-time experiments on the chambers and collect your own datasets with our Remote Lab.

The datasets are publicly available through a permissive CC BY 4.0 license. This means you are free to use, share and modify the datasets as long as you give appropriate credit and communicate changes. If you use the datasets in your scientific work, please consider citing:

@article{gamella2025chamber,
  author={Gamella, Juan L. and Peters, Jonas and B{\"u}hlmann, Peter},
  title={Causal chambers as a real-world physical testbed for {AI} methodology},
  journal={Nature Machine Intelligence},
  doi={10.1038/s42256-024-00964-x},
  year={2025},
}

Here you can also find the resources to build the chambers (see hardware/).

The code to reproduce the case studies in the original paper can be found in the separate paper repository.

See also the separate repository for the causalchamber package, which allows you to directly download datasets to your Python code, load ground-truth graphs, access the remote API, and use the physical simulators of the chambers.

Need help?

If you need help choosing the right dataset for your work, please write us an email.

Available datasets

We are open to suggestions of additional experiments that may prove interesting; please reach out via email.

Each dataset is described in detail in its corresponding page (click the dataset name), together with the download instructions. The chamber configurations are described in Fig. 3 of the manuscript.

Dataset name	Notes	Chamber	Config.
lt_crl_benchmark_v1	Datasets for the 2025 benchmark paper "Sanity Checking Causal Representation Learning on a Simple Real-World System" by Juan L. Gamella*, Simon Bing*, and Jakob Runge.	Light tunnel	camera
lt_camera_walks_v1	Image data for the ICA case study (task d3, Fig. 6).	Light tunnel	camera
lt_color_regression_v1	Image data for task b2 in the OOD case study (Fig. 5)	Light tunnel	camera
lt_interventions_standard_v1	Observational and interventional data from the light tunnel, used for the causal discovery case study in Fig. 5.	Light tunnel	standard
lt_walks_v1	Random and deterministic walks of the light-tunnel actuators. Used in the ICA case study (task d1), Fig. 6.	Light tunnel	standard
wt_walks_v1	Random and deterministic walks of the wind-tunnel actuators. Used in the causal discovery (task a3) and ICA (task d2) case studies.	Wind tunnel	standard
lt_malus_v1	Measurements of light intensity displaying Malus' law, used in the symbolic regression task in Fig. 6e.	Light tunnel	standard
wt_bernoulli_v1	Measurements of air pressure displaying Bernoulli's principle, used in the symbolic regression task in Fig. 6e.	Wind tunnel	standard
wt_changepoints_v1	Used for the change point detection case study in Fig. 5.	Wind tunnel	standard
wt_intake_impulse_v1	Barometric pressure curves used in task 2c, Fig. 5.	Wind tunnel	standard
wt_pressure_control_v1	Data from the pressure-control configuration of the wind tunnel.	Wind tunnel	pressure-control
lt_test_v1	Experiments to characterize some of the physical effects of the light tunnel. Shown in figures 7-15 of the manuscript.	Light tunnel	standard
wt_test_v1	Experiments to characterize some of the physical effects of the wind tunnel. Shown in figures 7-15 of the manuscript.	Wind tunnel	standard
lt_camera_test_v1	Experiments to characterize some of the physical effects of the camera system in the light tunnel.	Light tunnel	camera
wt_validate_v1	Randomized control experiments to validate the causal ground-truth graph of the wind tunnel in its standard configuration (appendix V of the manuscript).	Wind tunnel	standard
wt_pc_validate_v1	Randomized control experiments to validate the causal ground-truth graph of the wind tunnel in its pressure-control configuration (appendix V of the manuscript).	Wind tunnel	pressure-control
lt_validate_v1	Randomized control experiments to validate the causal ground-truth graphs of the light tunnel in its standard configuration (appendix V of the manuscript).	Light tunnel	standard
lt_camera_validate_v1	Randomized control experiments to validate the causal ground-truth graphs of the light tunnel in its camera configuration (appendix V of the manuscript).	Light tunnel	standard
lt_camera_v1	Image datasets where the light-tunnel actuators are sampled from different distributions and structural causal models.	Light tunnel	camera

Downloading the datasets

If you use Python, you can directly import a dataset into your code through the causalchamber package. For example, you can load the lt_camera_test_v1 image dataset as follows:

import causalchamber.datasets as datasets

# Download the dataset and store it, e.g., in the current directory
dataset = datasets.Dataset(name='lt_camera_test_v1', root='./', download=True)

# Select an experiment and load the observations and images
experiment = dataset.get_experiment(name='palette')

observations = experiment.as_pandas_dataframe()
images = experiment.as_image_array(size='200')

See each dataset page for a tailored example (e.g., here), and the package repository for more details & documentation.

You can also download a .zip file with all the data, including the images at different resolutions. The link and checksum (to verify integrity) are available on the dataset pages (click on the dataset name in the table above).

Licenses

All images and .csv files in the datasets are licensed under a CC BY 4.0 license. A copy of the license can be found in LICENSE.txt.

Contributing

If you would like to make a (highly welcome!) contribution towards the costs of running this repository, you can do so as a Github sponsor.