- 1.研究范围
- 2.代表人物或团队
- 2.1国际统计学领域
- 2.2国际计算机领域
- 2.3国内代表人物
- 3.经典书籍
- 4.开源工具包
- 5.前沿算法
- 5.1因果发现
- 5.2因果推断
- 5.3因果解释
- 6.公开数据集
- 7.公开课
- 8.应用案例
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
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
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)
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
| 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 |
◆ 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 |
◆ 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
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
