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

DOI10.1214/20-STS804WikidataQ114599249 ScholiaQ114599249MaRDI QIDQ2218092

Rajarshi Mukherjee, Lin Liu, 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/2008.03288

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (1)

Assumption-lean falsification tests of rate double-robustness of double-machine-learning estimators

Cites Work

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• Some new asymptotic theory for least squares series: pointwise and uniform results
• Moment inequalities for U-statistics
• Variance function estimation in multivariate nonparametric regression with fixed design
• On nonparametric confidence intervals
• Minimax estimation of a functional on a structured high-dimensional model
• Semiparametric minimax rates
• On the minimax optimality and superiority of deep neural network learning over sparse parameter spaces
• On nearly assumption-free tests of nominal confidence interval coverage for causal parameters estimated by machine learning
• Discussion of ``On nearly assumption-free tests of nominal confidence interval coverage for causal parameters estimated by machine learning
• Bootstrapping and sample splitting for high-dimensional, assumption-lean inference
• Effect of mean on variance function estimation in nonparametric regression
• Higher order influence functions and minimax estimation of nonlinear functionals
• On Profile Likelihood

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