因果推断与提升建模：文献综述

皮埃尔·古铁雷斯 (Pierre Gutierrez)，让-伊夫·热拉迪 (Jean-Yves Gérardy)
第三届预测应用与API国际会议论文集，PMLR 67:1-13，2017。

摘要

提升建模指的是用于建模某种行为或治疗对客户结果的增量影响的一系列技术。因此，提升建模既是因果推断问题，也是机器学习问题。关于提升建模的文献分为三种主要方法——双模型方法、类别转换方法和直接建模提升。不幸的是，由于缺乏通用的因果推断框架和符号，评估这三种方法可能非常困难。在本文中，我们使用鲁宾 (1974) 的因果推断模型及其现代“计量经济学”符号，为这三种方法提供清晰的比较并推广其中一种。据我们所知，这是第一篇提供提升建模文献统一综述的论文。此外，我们的论文通过证明，在极限情况下，关于因果效应估计器的均方误差 (MSE) 公式最小化等效于在未观察到的治疗效应被修改后的目标变量替换的MSE最小化，为文献做出了贡献。最后，我们希望我们的论文对有兴趣将机器学习技术应用于商业环境以及其他领域（医学、社会学或经济学）的因果推断问题的研究人员有所帮助。

引用本文

BibTeX
@InProceedings{pmlr-v67-gutierrez17a, title = {Causal Inference and Uplift Modelling: A Review of the Literature}, author = {Gutierrez, Pierre and Gérardy, Jean-Yves}, booktitle = {Proceedings of The 3rd International Conference on Predictive Applications and APIs}, pages = {1--13}, year = {2017}, editor = {Hardgrove, Claire and Dorard, Louis and Thompson, Keiran and Douetteau, Florian}, volume = {67}, series = {Proceedings of Machine Learning Research}, month = {11--12 Oct}, publisher = {PMLR}, pdf = {https://pmlr.com.cn/v67/gutierrez17a/gutierrez17a.pdf}, url = {https://pmlr.com.cn/v67/gutierrez17a.html}, abstract = {Uplift modeling refers to the set of techniques used to model the incremental impact of an action or treatment on a customer outcome. Uplift modeling is therefore both a Causal Inference problem and a Machine Learning one. The literature on uplift is split into 3 main approaches - the Two-Model approach, the Class Transformation approach and modeling uplift directly. Unfortunately, in the absence of a common framework of causal inference and notation, it can be quite difficult to assess those three methods. In this paper, we use the Rubin (1974) model of causal inference and its modern “econometrics” notation to provide a clear comparison of the three approaches and generalize one of them. To our knowledge, this is the first paper that provides a unified review of the uplift literature. Moreover, our paper contributes to the literature by showing that, in the limit, minimizing the Mean Square Error (MSE) formula with respect to a causal effect estimator is equivalent to minimizing the MSE in which the unobserved treatment effect is replaced by a modified target variable. Finally, we hope that our paper will be of use to researchers interested in applying Machine Learning techniques to causal inference problems in a business context as well as in other fields: medicine, sociology or economics.} }
Endnote
%0 会议论文 %T 因果推断与提升建模：文献综述 %A Pierre Gutierrez %A Jean-Yves Gérardy %B 第三届预测应用与API国际会议论文集 %C 机器学习研究会议论文集 %D 2017 %E Claire Hardgrove %E Louis Dorard %E Keiran Thompson %E Florian Douetteau %F pmlr-v67-gutierrez17a %I PMLR %P 1--13 %U https://pmlr.com.cn/v67/gutierrez17a.html %V 67 %X Uplift modeling refers to the set of techniques used to model the incremental impact of an action or treatment on a customer outcome. Uplift modeling is therefore both a Causal Inference problem and a Machine Learning one. The literature on uplift is split into 3 main approaches - the Two-Model approach, the Class Transformation approach and modeling uplift directly. Unfortunately, in the absence of a common framework of causal inference and notation, it can be quite difficult to assess those three methods. In this paper, we use the Rubin (1974) model of causal inference and its modern “econometrics” notation to provide a clear comparison of the three approaches and generalize one of them. To our knowledge, this is the first paper that provides a unified review of the uplift literature. Moreover, our paper contributes to the literature by showing that, in the limit, minimizing the Mean Square Error (MSE) formula with respect to a causal effect estimator is equivalent to minimizing the MSE in which the unobserved treatment effect is replaced by a modified target variable. Finally, we hope that our paper will be of use to researchers interested in applying Machine Learning techniques to causal inference problems in a business context as well as in other fields: medicine, sociology or economics.
APA
古铁雷斯，P. & 热拉迪，J. (2017)。因果推断与提升建模：文献综述。第三届预测应用与API国际会议论文集，载于机器学习研究会议论文集 67:1-13。可从 https://pmlr.com.cn/v67/gutierrez17a.html 获取。

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