Augmented Minimax Linear Estimation
Methodology
2020-11-23 v6
Abstract
Many statistical estimands can expressed as continuous linear functionals of a conditional expectation function. This includes the average treatment effect under unconfoundedness and generalizations for continuous-valued and personalized treatments. In this paper, we discuss a general approach to estimating such quantities: we begin with a simple plug-in estimator based on an estimate of the conditional expectation function, and then correct the plug-in estimator by subtracting a minimax linear estimate of its error. We show that our method is semiparametrically efficient under weak conditions and observe promising performance on both real and simulated data.
Cite
@article{arxiv.1712.00038,
title = {Augmented Minimax Linear Estimation},
author = {David A. Hirshberg and Stefan Wager},
journal= {arXiv preprint arXiv:1712.00038},
year = {2020}
}
Comments
67 pages, 3 figures