AI RESEARCH
Kernel Treatment Effects with Adaptively Collected Data
arXiv CS.LG
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ArXi:2510.10245v2 Announce Type: replace-cross Adaptive experiments improve efficiency by adjusting treatment assignments based on past outcomes, but this adaptivity breaks the i.i.d.\ assumptions that underpin classical asymptotics. At the same time, many questions of interest are distributional, extending beyond average effects. Kernel treatment effects (KTE) provide a flexible framework by representing interventional outcome distributions in an RKHS and comparing them via kernel distances.