CBRE

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This is the official implementation of CBRE in the paper “Cycle-Balanced Representation Learning For Counterfactual Inference”.

The code is built on the Counterfactual regression (CFR) and Adversarial Balancing based representation learning for Causal Effect Inference (ABCEI) . The random parameter searching, network training and evaluation follow the procedures of CFR to ensure a fair comparison.

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Installation

We use Python 3.5.6 and Tensorflow==1.4.0. You can install libraries with requirements.txt.

pip install -r requirements.txt

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Usage

Data: there are three datasets in this repository, and you can also download them from The website of Dr. Fredrik D. Johansson.

Configs: there are three config.txt for datasets.

The implementation of the CBRE network is included in cbre/cbre_net.py.

The training code is cbre_train.py, and you can test performance with different parameters by cbre_param_search.py.

The overall procedure is the same as cfrnet and abcei.

==You can replace the class CBRENet in cbre/cbre_net.py with your model.==

About how to use param_search and evaluate, you can refer to CFRnet. We use evaluate.py to test performance on the IHDP and Jobs datasets. And we assess the Twins dataset with evaluate_twins.py for our model and baselines.

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Cite

Please consider citing this paper if it’s helpful for you.

@article{zhou2021cycle,
  title={Cycle-Balanced Representation Learning For Counterfactual Inference},
  author={Zhou, Guanglin and Yao, Lina and Xu, Xiwei and Wang, Chen and Zhu, Liming},
  journal={arXiv preprint arXiv:2110.15484},
  year={2021}
}