AI RESEARCH
Masked Unfairness: Hiding Causality within Zero ATE
arXiv CS.LG
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ArXi:2603.06984v1 Announce Type: cross Recent work has proposed powerful frameworks, rooted in causal theory, to quantify fairness. Causal inference has primarily emphasized the detection of \emph{average} treatment effects (ATEs), and subsequent notions of fairness have inherited this focus. In this paper, we build on previous concerns about regulation based on averages.