【Uplift】参考资料篇
https://zhuanlan.zhihu.com/p/358582762
博客等
- 腾讯广告-uplift广告增效衡量说明
https://e.qq.com/ads/adfaq/delivery/tool/08/
https://morketing.com/detail/4704
- 一文读懂Uplift
https://mp.weixin.qq.com/s/FOVggFduHKeDr3jidcmqgA
基本是综述论文翻译一遍
- 滴滴：DiDi Food中的智能补贴实战漫谈
https://mp.weixin.qq.com/s/WU1iILMFdH3RZAbJKFU4WA
树分裂的部分讲的比较详细，方便理解
- 因果推断简介（丁鹏）
https://yao-lab.github.io/2009.fall.pku/lecture10_DingP_causal091101.pdf
论文
- Causal Inference and Uplift Modeling A review of the literature
http://proceedings.mlr.press/v67/gutierrez17a.html
综述论文，很经典
- A Survey on Causal Inference
https://arxiv.org/abs/2002.02770
综述论文，比较新
- A Large Scale Benchmark for Uplift Modeling
http://ama.imag.fr/~amini/Publis/large-scale-benchmark.pdf
数据集，有AUUC和Qini的具体公式，注意和前面的综述不太一致
- Metalearners for estimating heterogeneous treatment effects using machine learning
https://arxiv.org/abs/1706.03461
X-Learner，有S-Learner和T-Learner的介绍
- Quasi-Oracle Estimation of Heterogeneous Treatment Effects
https://arxiv.org/abs/1712.04912
R-Learner
- Feature Selection Methods for Uplift Modeling
https://arxiv.org/abs/2005.03447
特征筛选方法
- Adapting Neural Networks for the Estimation of Treatment Effects
https://arxiv.org/abs/1906.02120
DragonNet
- Decision trees for uplift modeling with single and multiple treatments
https://link.springer.com/article/10.1007/s10115-011-0434-0
UpliftTree
- Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
http://bayes.acs.unt.edu:8083/BayesContent/class/rich/articles/Estimation_And_Inference_Of_Heterogeneous_Treatment_Effects_Using_Random_Forests.pdf
CausalForest
- Uplift Modeling with Multiple Treatments and General Response Types
https://arxiv.org/abs/1705.08492
CTS，一种tree-based的方法
- Free Lunch! Retrospective Uplift Modeling for Dynamic Promotions Recommendation within ROI Constraints
https://arxiv.org/abs/2008.06293
一个发券应用，据说可以只用正样本就可以
调包
- Causal ML
https://causalml.readthedocs.io/en/latest/about.html
Uber开源的一个包，实现了Meta-Learner和Tree-based方法、特征筛选、评估
- DoWhy
https://microsoft.github.io/dowhy/readme.html
微软的，没用过，看着还比较全
- 其他还有pylift、EconML，还有R的一些实现的包
其他资源
- coursera课程：A Crash Course in Causality: Inferring Causal Effects from Observational Data
https://www.coursera.org/learn/crash-course-in-causality/home/week/1
入门款
- awesome-causality-algorithms
https://github.com/rguo12/awesome-causality-algorithms
一个汇总资源的github
- 因果推断入门
https://www.bilibili.com/video/BV1sJ41177sg
B站，一个up对Causal Inference in Statistics: A Primer的解说，连载中
- Introduction to Causal Inference
https://www.bradyneal.com/causal-inference-course
一个大佬的因果推断课程