A testing platform to assess the performance of CATE estimators across popular datasets.

The easiest way to replicate the running environment is through Anaconda. Once installed, follow the steps below.

1. Download the repo.
2. Enter the directory (i.e. cd cate-benchmark).
3. Run the following command to recreate the 'cate-bench' conda environment:

conda env create -f environment.yml

4. Download datasets from here. Once downloaded, extract them to 'datasets' directory.

The 'experiments' folder contains a few example scripts that run the code, ranging from basic to more advanced. To learn more about available script parameters, see the contents of 'main.py'.

By default, any files created as part of running the scripts are saved under 'results'.

Go to 'experiments' and run the basic script:

bash basic.sh

This script tests the Lasso model against one iteration of the IHDP data set. You should see relevant metrics and the performance obtained by the estimator printed in the console. In the same script, it is easy to change the number of iterations, the data set or the estimator.

Go to 'experiments' and run the advanced script:

bash advanced.sh

This script covers more estimators and 10 iterations of IHDP. Once it's done, you should see a summary similar to the following one:

You can find the content of the summary in 'results/combined.csv'.

This script tests almost all estimators against all four data sets. Depending on the computational power of your machine, this script may take days or even weeks to complete. To run the script, go to 'experiments' and run:

bash extensive.sh

A separate directory is created per each selected estimator when running the scripts to store various results. The following result files are usually created:

• info.log (intermediate results as the script is being executed)
• scores.csv (final scores per relevant metric)
• times.csv (training prediction time in seconds consumed by an estimator)

In addition, it is possible to get a summary of multiple estimators in a single table. This can be done via the 'results/process.py' script, which in turn produces 'combined.csv' file. For an example usage, see some of the existing running scripts.

The code can be easily extended to use more estimators.

1. Go to 'main.py'.
2. Edit 'get_parser()': add new key to 'estimation_model'.
3. Edit '_get_model()': using the new key, return an instance of your model.
4. Edit 'estimate(): train your model on the data and provide predictions.

Projects using the CATE benchmark:

• Undersmoothing Data Augmentation