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.

翻译：本文讨论了在因果推断字段中应用沙普利值的问题,特别是如何以可缩放的方式选择粗化精确匹配方法的顶部混淆变量。 我们用LinkedIn成员观察实验的数据集来测试其适用性,并表明沙普利值信息量很高,并且可以用其强大的重要性排序能力加以利用。