Installation

Install the latest release from PyPi:

pip install causaltune

Note

Mac/ OS users: For some machines, it can happen that the package LightGBM which is a dependency of AutoML / Flaml will not automatically be installed properly. In that case, a workaround is to set up a conda environment and install LightGBM through the conda-forge channel:

conda create -n <my_env> python=3.9.16
conda activate <my_env>
pip install causaltune
conda install -c conda-forge lightgbm

Quick Start

The CausalTune package can be used like a scikit-style estimator:

from causaltune import CausalTune
from causaltune.datasets import synth_ihdp

# prepare dataset
data = synth_ihdp()
data.preprocess_dataset()


# init CausalTune object with chosen metric to optimise
ct = CausalTune(time_budget=600, metric="energy_distance")

# run CausalTune
ct.fit(data)

# return best estimator
print(f"Best estimator: {ct.best_estimator}")

For Developers

Clone this repository and run the following command from the top-most folder of the repository.

pip install -r requirements-dev.txt

This project uses pytest for testing. To run tests locally after installing the package, you can run

python setup.py pytest

Requirements

CausalTune requires the following packages:

• numpy

• pandas

• econml

• dowhy

• flaml

• scikit-learn

• matplotlib

• dcor

• wise-pizza

• seaborn

If you cloned the repository, they can be installed via

pip install -r requirements.txt