Confounder Analysis in Measuring Representation in Product Funnels
Machine Learning
2022-06-08 v1 Machine Learning
Abstract
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.
Cite
@article{arxiv.2206.02962,
title = {Confounder Analysis in Measuring Representation in Product Funnels},
author = {Jilei Yang and Wentao Su},
journal= {arXiv preprint arXiv:2206.02962},
year = {2022}
}
Comments
9 pages, 1 figure