Confounder Analysis in Measuring Representation in Product Funnels

Autor:	Yang, Jilei, Su, Wentao
Rok vydání:	2022
Předmět:	Statistics - Machine Learning Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	This paper discusses an application of Shapley values in the causal inference field, specifically on how to select the top confounder variables for coarsened exact matching method in a scalable way. We use a dataset from an observational experiment involving LinkedIn members as a use case to test its applicability, and show that Shapley values are highly informational and can be leveraged for its robust importance-ranking capability. Comment: 9 pages, 1 figure
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2206.02962 Zobrazit plný text záznamu View this record from Arxiv