In this review we cover the basics of efficient nonparametric parameter estimation (also called functional estimation), with a focus on parameters that arise in causal inference problems. We review both efficiency bounds (i.e., what is the best possible performance for estimating a given parameter?) and the analysis of particular estimators (i.e., what is this estimator's error, and does it attain the efficiency bound?) under weak assumptions. We emphasize minimax-style efficiency bounds, worked examples, and practical shortcuts for easing derivations. We gloss over most technical details, in the interest of highlighting important concepts and providing intuition for main ideas.
翻译:在本次审查中,我们涵盖了高效的非参数估计(也称为功能估计)的基本内容,重点是因果推断问题中产生的参数;我们既审查了效率界限(即估计某一参数的最佳性能是什么? ),又审查了在薄弱假设下对特定估计器的分析(即,这个估计器的错误是什么,它是否达到了效率约束? ),我们强调最小式效率界限、工作范例和缓解衍生的实用捷径。我们为强调重要概念和为主要想法提供直觉,对大多数技术细节都作了说明。