  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.
