Confounder Analysis in Measuring Representation in Product Funnels
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 12 references that are in the map. First posted on arXiv 7 Jun 2022.
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References · 5
- 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
- 2018Consistent Individualized Feature Attribution for Tree Ensemblescited by 285
- 2019Explaining black box decisions by Shapley cohort refinementcited by 26
- 2021Cohort Shapley value for algorithmic fairnesscited by 13
- 2021Measuring Equity: Funnel Representation Measurementcited by 1
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