Semiparametric Efficient Inference in Adaptive Experiments

arXiv:2311.18274v3 Announce Type: replace-cross
Abstract: We consider the problem of efficient inference of the Average Treatment Effect in a sequential experiment where the policy governing the assignment of subjects to treatment or control can change over time. We first provide a central limit theorem for the Adaptive Augmented Inverse-Probability Weighted estimator, which is semiparametric efficient, under weaker assumptions than those previously made in the literature. This central limit theorem enables efficient inference at fixed sample sizes. We then consider a …

abstract arxiv change control cs.lg experiment inference inverse-probability policy probability stat.me stat.ml theorem treatment type