论文标题
半参数双重稳健的针对性双机器学习：评论
Semiparametric doubly robust targeted double machine learning: a review
论文作者
论文摘要
在这篇综述中，我们涵盖了有效的非参数参数估计（也称为功能估计）的基础，重点是在因果推理问题中出现的参数。我们审查效率界限（即，估计给定参数的最佳性能是什么？）和对特定估计器的分析（即，在弱假设下，该估计器的错误是什么，并且是否达到了效率？）。我们强调了最小值效率的范围，工作示例和宽松派生的实际快捷方式。为了强调重要概念并为主要思想提供直觉，我们掩盖了大多数技术细节。
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.
