README.md

CausalDA

Code to reproduce the numerical experiments in the paper "Domain adaptation under structural causal models" (https://arxiv.org/abs/2010.15764) by Yuansi Chen and Peter Bühlmann.

Code written with Python 3.7.1 and PyTorch version as follows

torch==1.2.0 torchvision==0.4.0

User Guide

• semiclass.py implements the main DA methods
  • semitorchclass, semitorchstocclass implement the same functions with PyTorch
  • semitorchMNISTclass is tailored to convolutional neural nets
• Linear SCM simulations: first seven experiments
  • sim_linearSCM_mean_shift_exp1-7.ipynb can run on a single core and plot
• Linear SCM simulations: last two experiments
  • Run the following simulations in a computer cluster
    • sim_linearSCM_var_shift_exp8_box_submit.py
    • sim_linearSCM_var_shift_exp8_scat_submit.py
    • sim_linearSCM_var_shift_exp9_scat_submit.py
  • Read the results and plot with sim_linearSCM_variance_shift_exp8-9.ipynb
• MNIST experiments:
  • Need to set the MNIST data folder!
  • Run mnist_get_pretrained.ipynb to get a pretrained CNN on original MNIST
  • Single source exp: run submit_simu_MNIST_patches_2M.py
  • Mutiple source exp: run submit_simu_MNIST_patches.py
  • Read the results and plot with MNIST_read_and_plot_whitepatch2M.ipynb and MNIST_read_and_plot_rotation5M.ipynb
• Amazon review dataset experiments
  • Need to set the Amazon review data folder!
  • Preprocess the data with read_and_preprocess_amazon_review_data_2018_subset.ipynb
  • Run the simulations with submit_amazon_review_data_2018_subset_regression.py
  • Plot with amazon_read_and_plot.ipynb

License and Citation

Code is released under MIT License. Please cite our paper if the code helps your research.

@article{chen2020domain,
    title={Domain adaptation under structural causal models},
    author={Chen, Yuansi and Peter B{\"u}hlmann},
    journal={arXiv preprint arXiv:2010.15764},
    year={2018}
}