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.

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.

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

Python package for causal discovery based on LiNGAM.

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

A list of Graph Causal Learning materials.

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

A Python package for causal inference using Synthetic Controls

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

🛠 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】

Causal Inference & Deep Learning, MIT IAP 2018

(Realtime) Temporal Convolutions in PyTorch

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

Codes for Causal Semantic Generative model (CSG), the model proposed in "Learning Causal Semantic Representation for Out-of-Distribution Prediction" (NeurIPS-21)

Uplift modeling and evaluation library. Actively maintained pypi version.

Streamline a data analysis process