AI RESEARCH
Semiparametric Efficient Test for Interpretable Distributional Treatment Effects
arXiv CS.LG
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ArXi:2605.08034v1 Announce Type: cross Distributional treatment effects can be invisible to means: a treatment may preserve average outcomes while changing tails, modes, dispersion, or rare-event probabilities. Kernel tests can detect discrepancies between interventional outcome laws, but global tests do not reveal where the laws differ. We propose DR-ME, to our knowledge the first semiparametrically efficient finite-location test for interpretable distributional treatment effects.