HyperSCI-KDD22: Learning Causal Effects on Hypergraphs

Code for the KDD 2022 paper Learning Causal Effects on Hypergraphs.

Environment

Python 3.6
Pytorch 1.2.0
Scipy 1.3.1
Numpy 1.17.2

Dataset

Demo datasets with simulation can be found in link.

Run Experiment

HyperSCI

python HyperSCI.py --dataset 'contact' --path '../data/contact.mat'

With the demo contact.mat dataset and default parameter settings, the mean results ($\sqrt{\epsilon_{PEHE}}$ and $\epsilon_{ATE}$) of three runs for our method should be $12.16/9.55$.

python HyperSCI.py --dataset 'GoodReads' --path '../data/GoodReads.mat'

With the demo GoodReads.mat dataset and default parameter settings, the mean results ($\sqrt{\epsilon_{PEHE}}$ and $\epsilon_{ATE}$) of three runs for our method should be $33.30/4.73$.

The data preprocessing from raw data and simulation is in:

Data Preprocessing

python data_preprocessing.py

Data Simulation

python data_simulation.py

References

Jing Ma, Mengting Wan, Longqi Yang, Jundong Li, Brent Hecht, Jaime Teevan, “Learning Causal Effects on Hypergraphs”, ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2022.