CausalFM
PyTorch Implementation on Paper [ICLR2026]: Foundation Models for Causal Inference via Prior-Data Fitted Networks
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Download Latest Version (.zip)- frontdoor_test_dataset_1.csv
- frontdoor_test_dataset_10.csv
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- frontdoor_test_dataset_3.csv
- frontdoor_test_dataset_4.csv
- frontdoor_test_dataset_5.csv
- frontdoor_test_dataset_6.csv
- frontdoor_test_dataset_7.csv
- frontdoor_test_dataset_8.csv
- frontdoor_test_dataset_9.csv
- causaldatasetFD.py
- causaldatasettestFD.py
- gen_frontdoor.py
- normalize_data.py
- iv_binary_test_dataset_1.csv
- iv_binary_test_dataset_10.csv
- iv_binary_test_dataset_2.csv
- iv_binary_test_dataset_3.csv
- iv_binary_test_dataset_4.csv
- iv_binary_test_dataset_5.csv
- iv_binary_test_dataset_6.csv
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- iv_binary_test_dataset_8.csv
- iv_binary_test_dataset_9.csv
- iv_conti_test_dataset_1.csv
- iv_conti_test_dataset_10.csv
- iv_conti_test_dataset_2.csv
- iv_conti_test_dataset_3.csv
- iv_conti_test_dataset_4.csv
- iv_conti_test_dataset_5.csv
- iv_conti_test_dataset_6.csv
- iv_conti_test_dataset_7.csv
- iv_conti_test_dataset_8.csv
- iv_conti_test_dataset_9.csv
- causaldatasetIV.py
- causaldatasettestIV.py
- gen_iv_data_binary.py
- gen_iv_data_conti.py
- normalize_data.py
- jobs_data.csv
- .DS_Store
- cate_test_dataset_1.csv
- cate_test_dataset_10.csv
- cate_test_dataset_2.csv
- cate_test_dataset_3.csv
- cate_test_dataset_4.csv
- cate_test_dataset_5.csv
- cate_test_dataset_6.csv
- cate_test_dataset_7.csv
- cate_test_dataset_8.csv
- cate_test_dataset_9.csv
- causaldataset.py
- causaldatasettest.py
- gen_standard_syn.py
- normalize_data.py
- test_fd.ipynb
- test_iv_binary.ipynb
- test_iv_conti.ipynb
- test_jobs.ipynb
- test_standard_cate.ipynb
- _sklearn_compat.py
- debug_versions.py
- __init__.py
- bar_distribution.py
- causalFM.py
- causalFM4FD.py
- causalFM4IV.py
- config.py
- encoders.py
- layer.py
- loading.py
- memory.py
- mlp.py
- multi_head_attention.py
- preprocessing.py
- transformer.py
- training_fd.py
- training_iv_binary.py
- training_iv_conti.py
- training_standard.py
- __init__.py
- base.py
- classifier.py
- config.py
- constants.py
- inference.py
- preprocessing.py
- regressor.py
- utils.py
- .gitignore
- LICENSE
- README.md
- requirements.txt
# Installation Guide
1. Get the code
git clone https://github.com/yccm/CausalFM
Downloads the entire project code from GitHub to your computer.
cd CausalFM
Moves into the project folder you just downloaded.
2. Python
Easy RecommendedPrerequisites
pip install -r requirements.txt
Installs the Python libraries listed in requirements.txt (or similar).
β βββ test_fd.ipynb # Jupyter notebook: FD evaluation
Type this command into your terminal and run it.
β βββ test_iv_binary.ipynb # Jupyter notebook: Binary IV evaluation
Type this command into your terminal and run it.
β βββ test_iv_conti.ipynb # Jupyter notebook: Continuous IV evaluation
Type this command into your terminal and run it.
β βββ test_jobs.ipynb # Jupyter notebook: Jobs dataset evaluation
Type this command into your terminal and run it.
If it runs without errors and prints output in the terminal, it worked.
Pulled directly from this repo's README.
// repository documentation
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