【贝叶斯规则因果推理】《Causal Inference with Bayes Rule》 https://gradientinstitute.org/blog/6/
The Book of Why http://bayes.cs.ucla.edu/WHY/
【Ananke: Python因果推理包】 https://ananke.readthedocs.io/en/latest/index.html
【sbi：PyTorch模拟推理包】 https://github.com/mackelab/sbi
Learning Causal Models Online，通过在线学习学习因果推断 https://www.aminer.cn/pub/5ee8986891e011e66831c2a6/learning-causal-models-online
【《统计学中的因果推断》随书代码】’Causal Inference In Statistics' https://github.com/DataForScience/Causality
因果相关文献列表 https://github.com/fulifeng/Causal_Reading_Group
因果推理相关资源大列表(书/课程/视频/工具) https://github.com/imirzadeh/awesome-causal-inference
causal-curve：Python因果推理库 https://github.com/ronikobrosly/causal-curve
因果推理与自然语言处理交叉文献资源列表 https://github.com/causaltext/causal-text-papers
“理解”感知输入序列：用无监督程序合成解答智力测验问题，从很少量数据中产生可解释、人类可读的因果论 https://github.com/RichardEvans/apperception
Official Repository for ECCV 2020 paper "AiR: Attention with Reasoning Capability" https://github.com/szzexpoi/AiR
Implementation for NeurIPS oral paper: Causal Intervention for Weakly-Supervised Semantic Segmentation https://github.com/ZHANGDONG-NJUST/CONTA
Tensorflow 2 implementation of Causal-BERT https://github.com/vveitch/causal-text-embeddings-tf2
Pytorch implementation of "Adapting Text Embeddings for Causal Inference" https://github.com/rpryzant/causal-bert-pytorch
Towards Causal Representation Learning https://arxiv.org/abs/2102.11107
因果推理和机器(深度)学习“必读”论文资源列表 https://github.com/jvpoulos/causal-ml
行为背后的动力是什么？——智能体的因果机制 https://medium.com/@deepmindsafetyresearch/what-mechanisms-drive-agent-behaviour-e7b8d9aee88
计算机视觉因果关系相关文献列表 github.com/Wangt-CN/Awesome-Causality-in-CV
github.com/DataForScience/CausalInference
github.com/FenTechSolutions/CausalDiscoveryToolbox
神经逻辑相关文献资源列表 github.com/FLHonker/Awesome-Neural-Logic
《Causal Inference on Recommender Systems》 github.com/yixinwang/causal-recsys-public
《A causal view of compositional zero-shot recognition》(NeurIPS 2020) github.com/nv-research-israel/causal_compSubramaniKrishna/point-cloud-audio
CausalNLP：文本因果推理实用工具包 github.com/amaiya/causalnlp
深度学习因果推理模实例教程 Deep Learning Models for Causal Inference (under selection on observables) - Extensive tutorials for learning how to build deep learning models for causal inference using selection on observables in Tensorflow 2.
github.com/kochbj/Deep-Learning-for-Causal-Inference
Causality for NLP Reading List：NLP因果文献列表 github.com/zhijing-jin/Causality4NLP_Papers
PyCID: 基于pgmpy的因果影响图库 github.com/causalincentives/pycid
github.com/causal-machine-learning/kdd2021-tutorial
免费书：因果推理轻松入门 matheusfacure.github.io/python-causality-handbook/landing-page.html
免费书(讲义)：《因果推理导论：机器学习视角》 https://www.bradyneal.com/Introduction_to_Causal_Inference-Dec17_2020-Neal.pdf
神经网络算法推理 https://www.cell.com/patterns/pdf/S2666-3899(21)00099-4.pdf
因果推断教程资料 github.com/rmcelreath/causal_salad_2021 https://www.bilibili.com/video/BV1Kq4y1o74E/
Deep End-to-end Causal Inference https://arxiv.org/abs/2202.02195
Yann LeCun：世界模型——让人工智能系统像动物和人类一样学习、推理 https://ai.facebook.com/blog/yann-lecun-advances-in-ai-research
Causal Discovery Toolbox：因果发现工具箱，支持图和成对因果推理 github.com/FenTechSolutions/CausalDiscoveryToolbox
DoWhy：端到端因果推理库 github.com/py-why/dowhy
【DiCE：为机器学习模型生成多样化反事实解释】'DiCE - Generate Diverse Counterfactual Explanations for any machine learning model.' by InterpretML GitHub: github.com/interpretml/DiCE
【Auto-Causality: 自动化因果推理库】’Auto-Causality: A library for automated Causal Inference model estimation and selection - AutoML for causal inference.' by TransferWise Ltd. GitHub: github.com/transferwise/auto-causality
【YLearn：因果学习算法工具包，主要支持因果学习任务中的各类相关任务，从因果效应识别，到因果效应估计，到因果发现等等】' - YLearn, a pun of "learn why", is a python package for causal inference' by DataCanvas GitHub: github.com/DataCanvasIO/YLearn
【UCI《因果推理》课程】《CS 295 - Spring 2021, Causal Reasoning | UCI》by Rina Dechter https://www.ics.uci.edu/~dechter/courses/ics-295cr/spring-2021/?continueFlag=7aa1e0f49440de8613ea25f1f8e384fb
【因果推理可视化指南集锦】’Visual Guides for Causal Inference - A collection of visual guides to help applied scientists learn causal inference.' by Kat Hoffman GitHub: github.com/kathoffman/causal-inference-visual-guides
【Project Causica：开发机器学习解决方案以进行高效的决策，在跨领域达到人类专家水准】’Project Causica - aims to develop machine learning solutions for efficient decision making that demonstrate human expert-level performance across all domains' by Microsoft GitHub: github.com/microsoft/causica
【《机器学习与因果推断》课程资料】’Machine-Learning - Machine Learning and Causal Inference taught by Brigham Frandsen' by Mixtape-Sessions GitHub: github.com/Mixtape-Sessions/Machine-Learning
现代因果推理入门 https://alejandroschuler.github.io/mci/
【用串流图入门因果推理】《An introduction to causal inference via string diagrams》 https://piedeleu.com/posts/diagrammatic-causal-inference/