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：本综述涵盖了高效非参数参数估计（亦称函数型估计）的基础知识，重点聚焦于因果推断问题中产生的参数。在弱假设条件下，我们既回顾了效率界（即给定参数估计的理论最优表现），也分析了特定估计量的性质（即该估计量的误差是否达到效率界）。我们着重阐释极小化极大效率界、实例演算及简化推导过程的实用技巧。为突出核心概念并阐释主要思想背后的直观逻辑，本文省略了多数技术细节。