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.

Python library for causal inference. Supports causal discovery, identification, effect estimation, prediction, and simulation with a scikit-learn style API.

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.

A Python package for modular causal inference analysis and model evaluations

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

Python package for Causal Discovery by learning the graphical structure of Bayesian networks. Structure Learning, Parameter Learning, Inferences, Sampling methods.

Python package for causal discovery based on LiNGAM.

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

A resource list for causality in statistics, data science and physics

A list of Graph Causal Learning materials.

A Python package for causal inference using Synthetic Controls

This repository contains the dataset and the PyTorch implementations of the models from the paper Recognizing Emotion Cause in Conversations.

Python package for the creation, manipulation, and learning of Causal DAGs

(Realtime) Temporal Convolutions in PyTorch

🛠 How to Apply Causal ML to Real Scene Modeling？How to learn Causal ML？【✔从Causal ML到实际场景的Uplift建模】

The official implementation for ICLR22 paper "Handling Distribution Shifts on Graphs: An Invariance Perspective"

Causal Inference & Deep Learning, MIT IAP 2018

Streamline a data analysis process

Uplift modeling and evaluation library. Actively maintained pypi version.