AI RESEARCH
Stop Suppressing the Tail: Causal Inference for Extreme Events
arXiv CS.LG
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ArXi:2605.27474v1 Announce Type: cross Estimating how an outcome responds to a continuous treatment (the Average Dose-Response Function, or ADRF) is a core causal-inference primitive. However, when outcomes possess heavy tails, standard robust double machine learning (DML) deliberately suppresses these extremes to stabilize the bulk average. In high-stakes settings, such as financial returns or climate losses, this omitted 1-in-1000 extreme event is the actual target quantity.