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Efficient Full Causal Graph Discovery Using Large Language Models

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# Efficient Causal Graph Discovery Using Large Language Models
Repository for [Efficient Causal Graph Discovery Using Large Language Models](https://arxiv.org/abs/2402.01207) (Jiralerspong, Chen, et. al, 2024)

We propose a novel framework that leverages LLMs combined with breadth-first search (BFS) for full causal graph discovery. While **previous LLM-based methods require a quadratic number of pairwise queries, our work only requires a linear number of queries** and outperforms all baselines on graphs of various sizes while requiring no observational data. In addition, when available, our framework can easily incorporate observational data to improve performance.

Code to reproduce all experiments is available in this repository. The code should also be easily extendable to be applied to causal graphs from a variety of domains.

## Citing this repository

If you use the code or ideas from the paper for your own research, please cite 
```
@misc{jiralerspong2024efficient,
      title={Efficient Causal Graph Discovery Using Large Language Models}, 
      author={Thomas Jiralerspong and Xiaoyin Chen and Yash More and Vedant Shah and Yoshua Bengio},
      year={2024},
      eprint={2402.01207},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
```

## Setup
`conda env create -f environment.yml`

`conda activate LLM_for_causal`

If you want to run LLM methods: put your OpenAI API key in the appropriate section in `algs/llm/bfs.py` and `algs/llm/pairwise.py`

## Reproducing Results
To reproduce the results from the paper, run

`python run.py --dataset DATASET_NAME --alg ALG_NAME --n_samples N_SAMPLES`

with the desired `DATASET_NAME`, `ALG_NAME`, and `N_SAMPLES`

The results will be saved in `./logs/DATASET_NAME/N_SAMPLES/alg.json`
The supported values for `DATASET_NAME` are:
- `asia`
- `child`
- `neuropathic`

The supported values for `ALG_NAME` are:
- `pc`
- `ges`
- `notears`
- `dagma_linear`
- `llm_pairwise` (Note that to run `llm_pairwise` you must provide a valid OpenAI API key in `algs/llm/pairwise.py`)
- `llm_pairwise_with_statistics` (Note that to run `llm_pairwise_with_statistics` you must provide a valid OpenAI API key in `algs/llm/pairwise.py`)
- `llm_bfs` (Note that to run `llm_bfs` you must provide a valid OpenAI API key in `algs/llm/bfs.py`)
- `llm_bfs_with_statistics` (Note that to run `llm_bfs_with_statistics` you must provide a valid OpenAI API key in `algs/llm/bfs.py`)

The supported values for `N_SAMPLES` are:
- 100
- 1000
- 10000

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