Semiparametric doubly robust targeted double machine learning: a review
By Edward H. Kennedy. 2022
310 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Computer Networks and Communications, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/6883ca3ed0dc59935a6fdf566079c6d0036c09ce
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In Artificial Intelligence
In Statistics and Probability
Citations in the corpus
- Towards optimal doubly robust estimation of heterogeneous causal effects (2020, methodology): “For a review of influence functions and semipara-metric theory we refer to van der Laan and Robins [2003], Tsiatis [2006], and Kennedy [2022].”