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

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

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

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

Causal Discovery in Python. It also includes (conditional) independence tests and score functions.

推荐/广告/搜索领域工业界经典以及最前沿论文集合。A collection of industry classics and cutting-edge papers in the field of recommendation/advertising/search.

❗ uplift modeling in scikit-learn style in python 🐍

A Python package for modular causal inference analysis and model evaluations

Auton Survival - an open source package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Events

YLearn, a pun of "learn why", is a python package for causal inference

Python Causal Impact Implementation Based on Google's R Package. Built using TensorFlow Probability.

DoubleML - Double Machine Learning in Python

Code for the Recsys 2018 paper entitled Causal Embeddings for Recommandation.

Open-source Python library for statistical analysis of randomised control trials (A/B tests)

Implementation of Deep IV: A Flexible Approach for Counterfactual Prediction

Python package for causal discovery based on LiNGAM.

A Python package for causal inference in quasi-experimental settings

A Python package for causal inference using Synthetic Controls

CausalLift: Python package for causality-based Uplift Modeling in real-world business