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TabCausal
TabCausal is a tabular causal discovery model for predicting directed causal graphs from tabular data.
This repository hosts the released TabCausal model checkpoint. The source code, inference scripts, benchmark utilities, and examples are available at:
https://github.com/LAMDA-Tabular/TabCausal
Checkpoint
checkpoints/tabcausal-base.pt
Installation
git clone https://github.com/LAMDA-Tabular/TabCausal.git
cd TabCausal
pip install -r requirements.txt
Usage
python -m tabcausal.cli predict \
--checkpoint checkpoints/tabcausal-base.pt \
--input /path/to/data.npz \
--output outputs/prediction.npz \
--device cuda:0
The output file contains directed-edge logits, probabilities, and a predicted adjacency matrix.
TabCausal supports benchmark-style .npz files and common numeric tabular formats such as .csv, .tsv, .npy, .parquet, and .pkl.
Benchmark Evaluation
Benchmark generation and evaluation scripts are included in the GitHub repository. Please refer to the GitHub README for detailed instructions.
Citation
Citation information will be added when available.