On nearly assumption-free tests of nominal confidence interval coverage for causal parameters estimated by machine learning

DOI10.1214/20-STS786WikidataQ114599259 ScholiaQ114599259MaRDI QIDQ2218090

Lin Liu, Rajarshi Mukherjee, James M. Robins

Publication date: 12 January 2021

Published in: Statistical Science (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1904.04276

zbMATH Keywords

U-statisticscausal inferencevalid inferenceassumption-freehigher-order influence functions

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items

Nested doubly robust estimating equations for causal analysis with an incomplete effect modifier ⋮ Assumption-lean falsification tests of rate double-robustness of double-machine-learning estimators ⋮ Discussion of ``On nearly assumption-free tests of nominal confidence interval coverage for causal parameters estimated by machine learning ⋮ Rejoinder: On nearly assumption-free tests of nominal confidence interval coverage for causal parameters estimated by machine learning ⋮ Rejoinder: Optimal Individualized Decision Rules Using Instrumental Variable Methods

Uses Software

• AdaBoost.MH
• ImageNet
• AlexNet

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