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Discussion of "On nearly assumption-free tests of nominal confidence interval coverage for causal parameters estimated by machine learning" by Kennedy, Edward H.; Balakrishnan, Sivaraman; Wasserman, Larry A. is a scholarly article available to read on EtoBox.

What is Discussion of "On nearly assumption-free tests of nominal confidence interval coverage for causal parameters estimated by machine learning" about?

We congratulate the authors on their exciting paper, which introduces a novel idea for assessing the estimation bias in causal estimates. Doubly robust estimators are now part of the standard set of tools in causal inference, but a typical analysis stops with an estimate and a confidence interval. The authors give an approach for a unique type of model-checking that allows the user to check whether the bias is sufficiently small with respect to the standard error, which is generally required for confidence intervals to be reliable.

Author

Kennedy, Edward H.; Balakrishnan, Sivaraman; Wasserman, Larry A.

Published

2020

Language

EN