Experimental code for "Uplift modeling via Gradient Boosting paper"

This repository is created to reproduce the experiments of the paper. It contains the scripts to run the experiments and Jupyter notebooks to launch and analyze the results.

Requirements

The proposed method is based on the frameworks that requires Nvidia GPU. We used 24 Cores CPU, 512 GB RAM, and 2xTesla V100 to obtain the experimental results, but it is possible to execute with less hardware.

To setup the environment you need to have conda installed. After that, please execute the following to install all the dependencies (it is assumed that you run everything from the repository root directory):

bash ./setup_env.sh

After that rapids-24.04 conda env will be created in the repository root dir. You need to run all the experiments under this env. You can activate it by executing conda activate -p ./rapids-24.04

Data

To download and preprocess all the data please execute

python datasets/get_data.py

Synthetic experiment

Please run Synthetic.ipynb notebook to obtain the results provided in Section 4.1

Main experiment

The main experiment provided in Section 4.2 is separated on the two parts:

• GPU based experiments contain the proposed method together with Neural Network baselines. You can execute it by running RunGPUTasks.ipynb. Please, adjust the Params cell according to your hardware. The proposed results were obtained with the listed above hardware

• CPU based experiments Meta Learners based algorithms and CausalForest. You can execute it by running RunCPUTasks.ipynb. Please, adjust the Params cell according to your hardware. The proposed results were obtained with the listed above hardware

Analyze the results

After evaluations are finalized, you can obtain the contents by running ResultsMain.ipynb notebook