Taking causal inference to the extreme!

The package is developed for treatment recommendation & pairwise treatment individual effect estimation (ITE/CATE/HTE) when multiple treatment/intervention options exist. The package is still under development.

A repo with functions for building various COMs and GCOMs quickly.

This project aims to implement meta machine learning algorithms for causal inference

Production-grade causal uplift modeling on 14M rows, benchmarks S-Learner, T-Learner, and FT-Transformer challengers on the Criteo dataset, with Optuna tuning, MLflow tracking, FastAPI + Docker + Google Cloud Run serving, and a Streamlit dashboard.