This repository contains the code to reproduce the case studies of the 2024 paper The Causal Chambers: Real Physical Systems as a Testbed for AI Methodology by Juan L. Gamella, Jonas Peters and Peter Bühlmann.
For the datasets, mechanistic models, datasheets and resources to build the chambers, please visit causalchamber.org.
The code is contained in well-documented Jupyter notebooks, which make use of the causalchamber package to load the pertaining datasets. The following table serves as an index.
| Path | Description |
|---|---|
case_studies/causal_discovery_iid.ipynb |
Case study for causal discovery from i.i.d. data (tasks a1 and a2 in Fig. 5) |
case_studies/causal_discovery_time.ipynb |
Case study for causal discovery from time-series data (task a3 in Fig. 5) |
case_studies/ood_sensors.ipynb |
Task b1 (sensor measurements) of the OOD-generalization case study. (Fig. 5b) |
case_studies/ood_images.ipynb |
Task b2 (images) of the OOD-generalization case study. (Fig. 5b) |
case_studies/ood_impulses.ipynb |
Task b3 (impulse-response curves) of the OOD-generalization case study. (Fig. 5b) |
case_studies/changepoints.ipynb |
Change point detection case study (Fig. 5c) |
case_studies/ica.ipynb |
Case study for independent component analysis (tasks d1, d2 and d3 in Fig. 6) |
case_studies/symbolic_regression.ipynb |
Symbolic regression case study (Fig. 6e) |
case_studies/mechanistic_models.ipynb |
Contains examples of running the mechanistic models described in appendix IV, including the code for the case study in (Fig. 6f) and the additional plots in appendix IV. |
plots_appendices.ipynb |
Contains the code to produce figures 7-15 in appendix III and compute the calibrated reference voltages in Table 3. |
causal_validation.ipynb |
Contains the code to produce Tables 5-8 in appendix V of the manuscript. |
The directory case_studies/src contains supporting code for the notebooks, such as a wrapper for UT-IGSP[1] and the code for the symbolic regression algorithm[2] used in the corresponding case study.
All code is shared under the MIT license. A copy of the license can also be found in LICENSE.txt.
[1] Squires, Chandler, Yuhao Wang, and Caroline Uhler. "Permutation-based causal structure learning with unknown intervention targets." Conference on Uncertainty in Artificial Intelligence. PMLR, 2020.
[2] Kamienny, Pierre-Alexandre, et al. "End-to-end symbolic regression with transformers." Advances in Neural Information Processing Systems 35 (2022): 10269-10281.