.png){kind=icon}
Return
AUGMENTED MINIMAX LINEAR ESTIMATION
.png){kind=icon}
OA
AI
DOI：10.1214/21-AOS2080.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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 plugin 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.
Keywords:
Causal inference
convex optimization
semiparametric efficiency
Journal
IF:
3.7
Papers:
2.8K
Citations:
2.9W
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSaveShareSaveAn event based implementation of a fractional order controller on a non-Newtonian transiting robot
PREAI
ShareSave
View more