mckinsey/causalnex

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.

Causal Discovery in Python. Learning causality from data.

Python Toolkit for Causal and Probabilistic Reasoning

An index of algorithms for learning causality with data

Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and causality.

Uplift modeling and causal inference with machine learning algorithms

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.

Probabilistic reasoning and statistical analysis in TensorFlow

A game theoretic approach to explain the output of any machine learning model.