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