The python implementation of paper Structural Hawkes Processes for Learning Causal Structure from Discrete-Time Event Sequences. (IJCAI 2023)
The running example of SHP is given below.
from SHP import SHP_exp
from utils import get_performance
likelihood, fited_alpha, fited_mu, real_edge_mat, real_alpha, real_mu = SHP_exp(n=20, sample_size=20000,
out_degree_rate=1.5,
mu_range_str="0.00005,0.0001",
alpha_range_str="0.5,0.7",
decay=5, model_decay=0.35, seed=0,
time_interval=5, penalty='BIC',
hill_climb=True, reg=0.85)
res = get_performance(fited_alpha, real_edge_mat)The environment config is given in environment.yml and your can create the environment using:
conda env create -f environment.yml
conda activate shpThe real world experiment is implemented in real_world_data_exp.py. The dataset used for this experiment, 18V_55N_Wireless.tar.gz, can be found on: PCIC Causal Discovery Competition 2021.
To run the real experiment, make sure to download the dataset and place Alarm.csv and DAG.npy in the appropriate directory.
If you find this useful for your research, we would be appreciated if you cite the following papers:
@inproceedings{qiao2023shp,
author = {Jie Qiao and
Ruichu Cai and
Siyu Wu and
Yu Xiang and
Keli Zhang and
Zhifeng Hao},
title = {Structural Hawkes Processes for Learning Causal Structure from Discrete-Time
Event Sequences},
booktitle = {Proceedings of the Thirty-Second International Joint Conference on
Artificial Intelligence, {IJCAI} 2023, 19th-25th August 2023, Macao,
SAR, China},
pages = {5702--5710},
publisher = {ijcai.org},
year = {2023},
url = {https://doi.org/10.24963/ijcai.2023/633}
}