  Inverse probability weighting (IPW) is a general tool in survey sampling and
causal inference, used both in Horvitz-Thompson estimators, which normalize by
the sample size, and H\'ajek/self-normalized estimators, which normalize by the
sum of the inverse probability weights. In this work we study a family of IPW
estimators, first proposed by Trotter and Tukey in the context of Monte Carlo
problems, that are normalized by an affine combination of these two terms. We
show how selecting an estimator from this family in a data-dependent way to
minimize asymptotic variance leads to an iterative procedure that converges to
an estimator with connections to regression control methods. We refer to this
estimator as an adaptively normalized estimator. For mean estimation in survey
sampling, this estimator has asymptotic variance that is never worse than the
Horvitz--Thompson or H\'ajek estimators, and is smaller except in edge cases.
Going further, we show that adaptive normalization can be used to propose
improvements of the augmented IPW (AIPW) estimator, average treatment effect
(ATE) estimators, and policy learning objectives. Appealingly, these proposals
preserve both the asymptotic efficiency of AIPW and the regret bounds for
policy learning with IPW objectives, and deliver consistent finite sample
improvements in simulations for all three of mean estimation, ATE estimation,
and policy learning.
