A panel focused on developments and applicability of causal modeling for algorithmic fairness. Given the increasing interest in the use of causality in machine learning (both in general and as a tool for bias mitigation), what is the potential for these approaches to be applied in the real world? What are the key challenges to putting these methods into practice?