  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.
