#计算机科学#DoWhy是微软开发的一个用于因果推断的Python库，旨在引发因果关系思考和分析
Python Library for Causal and Probabilistic Modeling using Bayesian Networks
#计算机科学#A Python library that helps data scientists to infer causation rather than observing correlation.
#算法刷题#Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.
#计算机科学#A Python package for modular causal inference analysis and model evaluations
#计算机科学#Must-read papers and resources related to causal inference and machine (deep) learning
#计算机科学#Python package for Causal Discovery by learning the graphical structure of Bayesian networks. Structure Learning, Parameter Learning, Inferences, Sampling methods.
#计算机科学#Python package for causal discovery based on LiNGAM.
YLearn, a pun of "learn why", is a python package for causal inference
#计算机科学#A resource list for causality in statistics, data science and physics
#计算机科学#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"
#博客#Causal Inference & Deep Learning, MIT IAP 2018
Uplift modeling and evaluation library. Actively maintained pypi version.
#计算机科学#Codes for Causal Semantic Generative model (CSG), the model proposed in "Learning Causal Semantic Representation for Out-of-Distribution Prediction" (NeurIPS-21)