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arXiv · 2203.06469

Semiparametric doubly robust targeted double machine learning: a review

Edward H. Kennedy
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Abstract

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.

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Edward H. Kennedy. 2022-03-12. Semiparametric doubly robust targeted double machine learning: a review. https://arxiv.org/abs/2203.06469

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