因果科学网络资源整理

1.研究范围

2.代表人物或团队

下面列举我个人关注比较多的大牛们~

2.1国际统计学领域

从左至右依次为[超链接为大牛主页]：
Jerzy Neyman
James M. Robins
Donald B. Rubin
Tyler J. VanderWeele
Paul R. Rosenbaum

2.2国际计算机领域

从左至右依次为[超链接为大牛主页]：

Judea pearl
Geoffrey Hinton
Yoshua Bengio
Guido W. Imbens
Susan Athey

Elias Bareinboim

2.3国内代表人物

从左至右依次为[超链接为大牛主页]：
耿直（北大）
周晓华（北大）
张坤(CMU)
丁鹏(Berkeley)
崔鹏(清华)

从左至右依次为[超链接为大牛主页]：
蔡瑞初(广东工业大学)
况琨(浙大)
黄碧薇(CMU PHD )
张含望(南洋理工)
郭若诚(香港城市大学)

3.经典书籍

因果科学中文书单整理及简介
因果科学英文书单整理及简介

4.开源工具包

包名	文档	语言
causaleffect	Tutorial on Causal Inference and Counterfactual Reasoning	R
Tetrad	TETRAD-AToolbox FOR CAUSAL DISCOVERY	R
dosearch		R
daggity	daggity document	R
pcalg	For evaluation of heterogeneous treatment effect estimators on common reference as well as synthetic data	R
bnlearn	An experimental sandbox for causal inference and decision making in dynamics	R
CausalImpact	CausalImpact: Inferring causal impact using structural time-series models	R
rEDM	rEDM file	R
DoWhy	Tutorial on Causal Inference and Counterfactual Reasoning	python
WhyNot	An experimental sandbox for causal inference and decision making in dynamics	python
CausalDiscoveryToolbox	Causal Discovery Toolbox: Uncover causal relationships in Python	python
Uber CausalML	Causalml: Python package for causal machine learning	python
JustCause	For evaluation of heterogeneous treatment effect estimators on common reference as well as synthetic data	python
Causal-cmd	Causal-cmd document	Python&JAVA

5.前沿算法

5.1因果发现

◆ Center for Causal Discovery

◆ HUAWEI Noah

◆ causal-discovery文章+算法实现（63）

5.2因果推断

参考https://github.com/rguo12/awesome-causality-algorithms

year	title	code
主题1	Variable Selection/Importance for Learning Causal Effects	1
2016	Variable importance through targeted causal inference	R
主题2	For Individual-level Treatment Effects (ITEs)	5
2019	Adapting Neural Networks for the Estimation of Treatment Effects	python
2018	GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets	python
2018	Perfect match: A simple method for learning representations for counterfactual inference with neural networks	python
2017	Causal effect inference with deep latent-variable models	python
2016	Learning representations for counterfactual inference	python
主题3	For Average-level Treatment Effects: ATE, ATT or ATC	2
2018	Approximate residual balancing: debiased inference of average treatment effects in high dimensions	R
2016	Doubly robust matching estimators for high dimensional confounding adjustment	R
主题4	For Continuous Treatment Effects	1
2020	causal-curve: A Python Causal Inference Package to Estimate Causal Dose-Response Curves	python
主题5	Learning Causal Effects with Multi-cause Data	1
2018	The blessings of multiple causes	python
主题6	Transfer Learning for Learning Causal Effects	1
2018	Transfer Learning for Estimating Causal Effects using Neural Networks
主题7	Instrumental Variables	2
2019	PDSLASSO: Stata module for post-selection and post-regularization OLS or IV estimation and inference	stata
2017	Deep iv: A flexible approach for counterfactual prediction	python
主题8	Learning Causal Effects under Spillover Effect/Interference	3
2021	Modeling the Momentum Spillover Effect for Stock Prediction via Attribute-Driven Graph Attention Networks	python
2020	Causal Inference under Networked Interference
2018	Linked Causal Variational Autoencoder for Inferring Paired Spillover Effects	python
主题9	Learning Causal Effects from Networked Observational Data	2
2020	Learning Individual Causal Effects from Networked Observational Data	python
2019	Using embeddings to correct for unobserved confounding	python
主题10	Learning Time Varying/Dependent Causal Effects	2
2018	Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks	python
2014	Targeted maximum likelihood estimation for dynamic and static longitudinal marginal structural working models	R
主题11	Heterogeneous Treatment Effects	3
2018	Metalearners for estimating heterogeneous treatment effects using machine learning	R
2017	Estimation and inference of heterogeneous treatment effects using random forests	R
2017	Some methods for heterogeneous treatment effect estimation in high-dimensions	R
主题12	Recommendation	3
2021	Disentangling User Interest and Conformity for Recommendation with Causal Embedding	python
2020	Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback	python
2019	Doubly Robust Joint Learning for Recommendation on Data Missing Not at Random
2019	Top-k off-policy correction for a REINFORCE recommender system	python
2018	Causal embeddings for recommendation	python
2018	Unbiased offline recommender evaluation for missing-not-at-random implicit feedback	python
2018	The Deconfounded Recommender: A Causal Inference Approach to Recommendation
2016	Recommendations as treatments: Debiasing learning and evaluation	python
主题13	Natural Language Processing	3
2019	Using Text Embeddings for Causal Inference	python
2018	Deconfounded lexicon induction for interpretable social science	python
2018	Challenges of Using Text Classifiers for Causal Inference	python
主题14	Counterfactual Fairness	1
2017	Counterfactual fairness	python
主题15	Reinforcement Learning	1
2018	Deconfounding reinforcement learning in observational settings	python
主题16	** Causality and GAN**	1
2017	CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training	python
主题17	Natural Language Processing	2
2018	Stable Prediction across Unknown Environments
2018	A Simple Algorithm for Invariant Prediction	Julia

5.3因果解释

◆ Explaining machine learning classifiers through diverse counterfactual explanations(2019)
code python

◆ Efficient search for diverse coherent explanations
code python

◆ Counterfactual explanations without opening the black box: Automated decisions and the GDPR

6.公开数据集

IHDP1
IHDP1 (setting A) simulated
IHDP2
Twins
Job Training
ACIC Benchmark
News
TCGA

7.公开课

Course: Causal inference for statistics, social and biomedical sciences(2021)

集智学园因果专题（2020，2021）

Introduction to Causal Inference Fall 2020 (Brady Neal)

Causal Inference and Machine Learning 2019 (Guido Imbens)

Falco J. Bargagli Stoffi Harvard (Postdoctoral) / IMT (Phd)

8.应用案例

快手因果推断与实验设计

视频计量经济学因果分析工具在快手中的应用

因果推断在阿里飞猪广告算法中的实践

淘票票因果应用

中国计算机学会（CCF）-滴滴大数据联合实验室

“CCF-蚂蚁科研基金”2021年度指南发布