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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.

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.

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

Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.

Algorithms for explaining machine learning models

A Python library that helps data scientists to infer causation rather than observing correlation.

Python code for part 2 of the book Causal Inference: What If, by Miguel Hernán and James Robins

Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.

A Python package for modular causal inference analysis and model evaluations