Welcome to CausalForge!

CausalForge is a Python package that provides a suite of modeling & causal inference methods using machine learning algorithms based on Elevence Health recent research. It provides convenient APIs that allow to estimate Propensity Score, Average Treatment Effect (ATE), Conditional Average Treatment Effect (CATE) or Individual Treatment Effect (ITE) from experimental or observational data.

This page introduces users to the package and documents its features.

Check out the package on github.

User Guide

• Getting Started
• Loading Real-World & Benchmarking Datasets with CausalForge
• BCAUSS
• DragonNet
• GANITE
• BCAUS

We are actively developing CausalForge, so sometimes the docs fall behind. The README is always up to date, as is the master branch. Therefore, consult those first. If you find discrepancies between the docs and the package, please still let us know!