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.

翻译：许多统计估计值可表述为有条件预期功能的连续线性功能,其中包括在无根据情况下的平均治疗效果,以及连续估值和个性化治疗的概括性处理效果。在本文中,我们讨论了估算此类数量的一般方法:我们首先根据对有条件预期功能的估计,用简单的插件估计值来计算,然后通过减去其错误的微缩最大线性估计值来纠正插件估计值。我们表明,我们的方法在薄弱条件下是半对称有效的,并观察到真实数据和模拟数据的有希望的性能。