treatment-effects

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 inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

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.

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

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

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.

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

A General Causal Inference Framework by Encoding Generative Modeling

Machine learning based causal inference/uplift in Python

R package for Bayesian meta-analysis models, using Stan

Methods for subgroup identification / personalized medicine / individualized treatment rules

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

Lightweight uplift modeling framework for Python

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

CRAN Task View: Causal Inference

🎯 🎲 Targeted Learning of the Causal Effects of Stochastic Interventions

Deep Treatment Learning (R)