causal-models 话题在 GitHub 上没有填写简介，这里按 Star 数汇总最相关的热门仓库。
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 Toolkit for Causal and Probabilistic Reasoning
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.
（实时）PyTorch 中的时间卷积
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
🛠 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】
简化数据分析流程。一本关于数据分析、因果推理和计量经济学的书，为几乎没有背景、想要培养统计直觉和实用R技能的读者撰写。
The official implementation for ICLR2022 paper "Handling Distribution Shifts on Graphs: An Invariance Perspective"
Causal Inference & Deep Learning, MIT IAP 2018
Scalable open-source software to run, develop, and benchmark causal discovery algorithms