Causal Preference Elicitation (CaPE)

Official code release for Causal Preference Elicitation (CaPE)
Edwin V. Bonilla, He Zhao, Daniel M. Steinberg (ICML 2026)

Implementation and maintenance of the CaPE codebase by Edwin V. Bonilla.

• Pre-print: https://arxiv.org/abs/2602.01483
• Repository: https://github.com/csiro-funml/CaPE

One-command reproduction

From a fresh clone (assuming python points to python3.14):

python -m venv .venv
source .venv/bin/activate

pip install -r requirements.txt
./reproduce_paper.sh

This runs the paper experiments and regenerates figures using the plotting pipeline.

Repository layout

• dag/, feedback/, generation/, inference/, likelihood/, metrics/, expts/ – core library modules
• experiments/ – paper experiment entrypoints
• scripts/ – helper scripts (cluster submission, plotting, misc)
• configs/ – reference hyperparameters (YAML)
• docs/ – dataset setup notes
• results/, figures/, data/ – generated artifacts (gitignored)

Key entrypoints (paper)

• Sachs (HITL): python experiments/run_sachs_hitl.py
• Synthetic (cluster): bash scripts/cluster/submit_synthetic_cluster.sh
• CausalBench (cluster): bash scripts/cluster/submit_causalbench_cluster.sh
• Plots from saved results: bash scripts/plots/generate_all_plots.sh

Existing results

Results ready to be parsed by scripts/generate_paper_figures.sh and regenerate the figures in the submission can be found in results_final.zip

Cluster Experiments

Scripts for launching the synthetic and causalbench experiments in the cluster are at scripts/cluster/submit_causalbench_cluster.sh and scripts/cluster/submit_synthetic_cluster.sh.

Citation

If you use this code, please cite:

@inproceedings{bonilla2026cape,
  title={Causal Preference Elicitation},
  author={Bonilla, Edwin V. and Zhao, He and Steinberg, Daniel M.},
  booktitle={International Conference on Machine Learning},
  year={2026},
  doi={10.48550/arXiv.2602.01483}
}

Citation metadata is also available via CITATION.cff.

License

Licensed under the Apache License 2.0.