Implementation of paper DESCN: Deep Entire Space Cross Networks for Individual Treatment Effect Estimation, which is accepted in SIGKDD 2022 Applied Data Science Track.
- Treatment effect: the term refers to the causal effect of a binary (0–1) variable on an outcome variable of scientific or policy interest.
- Control group: without treatment
- Treatment group: with treatment
- The counterfactual: we can not observe both treated and control responses in the exact same context
This paper proposes Deep Entire Space Cross Networks (DESCN) to model treatment effects from an end-to-end perspective.
DESCN captures the integrated information of the treatment propensity,
the response, and the hidden treatment effect through a cross network in a multi-task learning manner.
The execution process and results can be viewed in DeepModels_real_data.ipynb which contains the code for converting dataset from .csv format to .npz format.
All experiments uses GPU for training and CUDA Verson:11.4
main.py: main process for all models, except X-learner.
The codes determines whether to enable sub-loss by setting the weight to 0 or not. Different models will enable different sub-loss.
x_learner_main.py: main process for X-learner(NN based).
eval4real_data.py: evaluation process for LAZADA real dataset.
eval.py: evaluation process for ACIC2019 dataset.
search_parames.py: A serial scheduling tool, which can specify the model hyper-parameter file, evaluation code, dataset through the command line, and conveniently complete the whole process.
A convenient way to call training and evaluation code serially:
python search_params.py main.py eval4real_data.py ./conf4models/lzd_real_data/DESCN.txt 1 {path_to_train_npz} {path_to_test_npz}
More examples can be referred to code in DeepModels_real_data.ipynb
All models's hyper-parameters are saved in ./conf4models.
NOTE:
- All path related configuration options must be set an absolute path.
./results/lzd_realand./runsshould be created.
./data contain the real-world Production Dataset from E-commerce platform Lazada. and the synthetic data from the 2019 American Causal Inference Conferen(ACIC2019)
- python == 3.7
other python packages version are showed in requirements.txt.