DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal …

Must-read papers and resources related to causal inference and machine (deep) learning

Sklearn-style implementations of Neural Network-based Conditional Average Treatment Effect (CATE) Estimators.

Code for the WSDM '20 paper, Learning Individual Causal Effects from Networked Observational Data.

Statistical inference and graphical procedures for RD designs using local polynomial and partitioning regression methods.

Machine learning based causal inference/uplift in Python

OpenASCE (Open All-Scale Casual Engine) is a Python package for end-to-end large-scale causal learning. It provides causal discovery, causal effect estimation and attribution algorithms all in one package.

A General Causal Inference Framework by Encoding Generative Modeling

Implementation of paper DESCN, which is accepted in SIGKDD 2022.

R package for Bayesian meta-analysis models, using Stan

This repository provides R-code for the estimation of the conditional average treatment effect (CATE) using machine learning (ML) methods.

Methods for subgroup identification / personalized medicine / individualized treatment rules

Lightweight uplift modeling framework for Python

My collection of causal inference algorithms built on top of accessible, simple, out-of-the-box ML methods, aimed at being explainable and useful in the business context

Interpretable and model-robust causal inference for heterogeneous treatment effects using generalized linear working models with targeted machine-learning

BITES: Balanced Individual Treatment Effect for Survival data

Code for CIKM'18 paper, Linked Causal Variational Autoencoder for Inferring Paired Spillover Effects.

Deep Treatment Learning (R)