Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Time
_{T. Vanderschueren*, A. Curth*, W. Verbeke & M. van der Schaar (ICML 2023)}

This repository provides the code for the paper "Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Time" (ICML 2023).

The structure of the code is as follows:

TESAR-CDE/
|_ data/
|_ experiments/
  |_ config.yml                     # Training config
|_ src/
  |_ models/
    |_ CDE_model.py                 # Code for TE-CDE and TESAR-CDE
  |_ utils/
    |_ cancer_simulation.py         # Generate the latent data
    |_ data_utils.py                # Preprocess observational data
    |_ losses.py                    # Loss functions 
    |_ process_irregular_data.py    # Informatively sample latent data to get observational data
    |_ training_tools.py            # Early stopping, dropout
main.py                             # Main executable to run experiment
trainer.py                          # Process data as tensors, fit model, make predictions

Installation

The requirements.txt provides the necessary packages. All code was written for python 3.7.9.

Weights and Biases (W&B) is required to log the experiments.

Usage

The experiments can be run through the main.pyfile using the following arguments:

python main.py
[--obs_coeff [informativeness coefficient]]
[--intensity_cov [number of covariates influencing the intensity, but not the outcome]]
[--intensity_cov_only [only include the intensity covariates]]
[--max_intensity [maximal intensity, i.e., observation probability]]
[--results_dir [path to directory to store results]]
[--model_name [final model name]]
[--load_dataset [boolean whether to load a saved version of the dataset from file]]
[--use_transformed [boolean whether to sample data]]
[--experiment [name of experiment yml]]
[--data_path [path to experiment data if loading one from a location]]
[--kappa [kappa parameter for the Hawkes process]]
[--iterations [number of iterations for the experiment]]
[--save_raw_datapath [path to save the raw dataset, so it can be reused to speed things up]]
[--save_transformed_datapath [path to save the transformed dataset, so it can be reused to speed things up]]

Example usage to train TESAR-CDE (Multitask) for informativeness $\gamma=6$:

python main.py --obs_coeff=6 --intensity_cov_only=False --num_patients=200 --importance_weighting=True --multitask=False

Acknowledgements

Our code builds upon the code for TE-CDE (Seedat et al. 2022).

Seedat, N., Imrie, F., Bellot, A., Qian, Z., & van der Schaar, M. (2022, June). Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations. In International Conference on Machine Learning (pp. 19497-19521). PMLR.

Citing

Please cite our paper and/or code as follows:

@InProceedings{tesarcde2023,
  title = 	 {{Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Time}},
  author =       {Vanderschueren, Toon and Alicia, Curth and Verbeke, Wouter and van der Schaar, Mihaela},
  booktitle = 	 {Proceedings of the 40th International Conference on Machine Learning},
  year = 	 {2023},
  volume = 	 {202},
  series = 	 {Proceedings of Machine Learning Research},
  publisher =    {PMLR},
}