The codes are associated with the following paper:

Deep Causal Reasoning for Recommendations,
Yaochen Zhu, Jing Yi, Jiayi Xie and Zhenzhong Chen,
ArXiv Preprints 2022. [pdf]

Note! We have released a survey regarding causal inference in recommender system. Check it out! Causal Inference in Recommender Systems: A Survey of Strategies for Bias Mitigation, Explanation, and Generalization.

Note! To better understand Rubin and Pearl's causal framework discussed in this paper, check out our new repo that summarizes relevant books of and disputes between the two most prominent schools of causal inference. Moreover, Prof. Ruocheng Guo's repo includes a thorough archive of various causal inference algorithms, with a sub-section devoted especially for recommender systems.

The codes are written in Python 3.6.5.

• numpy == 1.16.3
• pandas == 0.21.0
• tensorflow-gpu == 1.15.0
• tensorflow-probability == 0.8.0

• Acquire the movielens-1m and amazon-vg datasets:
  The original datasets can be found [here] and [here].
  Preprocess the data with data_sim/raw/prepare_data.py.

• Preprocess the original dataset: cd to data_sim/raw folder, run
  python prepare_data.py --dataset Name --simulate {exposure, ratings}.

• Fit the exposure and rating distribution via VAEs: cd to data_sim folder, run
  python train.py --dataset Name --simulate {exposure, ratings}.

• Simulate the causal dataset under various confounding levels:
  python simulate.py --dataset Name --simulate {exposure, ratings}.

• The simulated datasets are in casl/data folder

• Split the simulated causal datasets into train/val/test:
  cd to casl_rec/data folder, run
  python preprocess.py --dataset Name --split 5.

• Train the exposure model, conduct predictive check:
  python train_exposure.py --dataset Name --split [0-4]

• Infer the subsititute confounders:
  python infer_subs_conf.py --dataset Name --split [0-4]

• Train the potential rating prediction model:
  python train_ratings.py --dataset Name --split [0-4]

• Predict the scores for hold-out users:
  python evaluate_model.py --dataset Name --split [0-4]

For advanced argument usage, run the code with --help argument.

@article{zhu2022deep,
  title={Deep Causal Reasoning for Recommendations},
  author={Zhu, Yaochen and Yi, Jing and Xie, Jiayi and Chen, Zhenzhong},
  journal={arXiv preprint arXiv:2201.02088},
  year={2022}
}