Section 3 uplift Modeling uplift建模就是估计CATE,难点在于 无法使用有监督模型训练,因为没有ground truth 三种方法 t-model:分别用干预组和空白组样本训练一个关于y的预测模型(比如:y为使用时长时,就是一个回归模型,y为是否活跃,就是一个二分类模型) Class Transformation:仅适用于二元类型的outcome,label = 1...
三种uplift建模方法 以下三种建模方法的W均是binary形式即 W∈0,1 ,只有发放treatment和不发之分,Y基本也是binary形式,如是否续订明年的短信服务、是否得肺癌病等,部分算法支持连续变量的Y。 双模型方法 双模型方法(Two-Model method,也被称作t-leaner)经常作为uplift建模的baseline模型,其核心思想是使用W=1和W=0...
Causal ML is a Python package that provides a suite of uplift modeling and causal inference methods using machine learning algorithms based on recent research [1]. It provides a standard interface that allows user to estimate the Conditional Average Treatment Effect (CATE) or Individual Treatment Ef...
Causal MLis a Python package that provides a suite of uplift modeling and causal inference methods using machine learning algorithms based on recent research[1]. It provides a standard interface that allows user to estimate the Conditional Average Treatment Effect (CATE) or Individual Treatment Effect...
[5] Pierre Gutierrez and Jean-Yves Gerardy. Causal inference and uplift modeling a review of the literature. JMLR: Workshop and Conference Proceedings 67, 2016. [6] Jason Hartford, Greg Lewis, Kevin Leyton-Brown, and Matt Taddy. Deep iv: a flexible approach for counterfactual prediction. In...
"Estimation and inference of heterogeneous treatment effects using random forests." Journal of the American Statistical Association 113.523 (2018): 1228-1242. Rzepakowski, P., & Jaroszewicz, S. (2012). Decision trees for uplift modeling with single and multiple treatments. Knowledge and Information...
Uplift modeling refers to approaches to quantify net difference in outcome between applying a treatment and not applying it to an individual. It is a typical causal inference problem which allowing us to design a refined decision rule that only targets those susceptible. The core difficulty of the...
Wager, Stefan, and Susan Athey. "Estimation and inference of heterogeneous treatment effects using random forests."Journal of the American Statistical Association113.523 (2018): 1228-1242. Rzepakowski, P., & Jaroszewicz, S. (2012). Decision trees for uplift modeling with single and multiple trea...
1.DoWhy: An end-to-end library for causal inference https://microsoft.github.io/dowhy/#dowhy-an-end-to-end-library-for-causal-inference 2.Causal ML: A Python Package for Uplift Modeling and Causal Inference with ML https://github.com/uber/causalml ...
On Pearl's Hierarchy and the Foundations of Causal Inference Bongers, Stephan, et al. "Foundations of structural causal models with cycles and latent variables." The Annals of Statistics 49.5 (2021): 2885-2915. 参考文献 1. Athey S, Imbens G....