Causal Discovery from Poisson Branching Structural Causal Model Using High-Order Cumulant with Path Analysis

The Python implementation of paper Causal Discovery from Poisson Branching Structural Causal Model Using High-Order Cumulant with Path Analysis. (AAAI 2024)

Usage

The running example of PBSCM is given below.

from PB_SCM import PB_SCM
from util import *

param_dict = {
    "n": 10,
    "seed": 2024,
    "in_degree_rate": 3.0,
    "sample_size": 30000,
    "alpha_range_str": "0.1,0.5",
    "mu_range_str": "1,3",
}

data, edge_mat, alpha_mat, mu = data_generate(**param_dict)

model = PB_SCM(data, seed=2024)
skeleton = model.Hill_Climb_search()

causal_graph = learning_causal_direction(data, skeleton)

Requirements

The requirements are given in requirements.txt. You can install them using the following command:

pip install -r requirements.txt

Citation

If you find this useful for your research, we would appreciate it if you could cite the following papers:

@inproceedings{qiao2024causal,
  title={Causal Discovery from Poisson Branching Structural Causal Model Using High-Order Cumulant with Path Analysis},
  author={Qiao, Jie and Xiang, Yu and Chen, Zhengming and Cai, Ruichu and Hao, Zhifeng},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={38},
  number={18},
  pages={20524--20531},
  year={2024}
}