Implementation of the paper - Predicting Dose-Response Curves with Deep Neural Networks, published at ICML 2024.

• run_all_experiments.py contains driver code for executing the experiments.
• train_model.py contains the training loop for the model
• optimize_hyperparameters.py contains the hyperparameter optimization pipeline.
• in utils.py utilities for downloading and pre-processing the data are contained
• In FunFor the code from FunFor [1] found at https://github.com/xiaotiand/FunFor was adapted to support multi-threading and covariates with 0 variance.

1. Fu, G., Dai, X., & Liang, Y. (2021). Functional random forests for curve response. Scientific Reports, 11(1), 1-14.