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

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

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arXiv · 2006.09613

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

Edward H. Kennedy·Sivaraman Balakrishnan·Larry A. Wasserman

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Abstract

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.

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Edward H. Kennedy, Sivaraman Balakrishnan, Larry A. Wasserman. 2020-06-17. Discussion of "On nearly assumption-free tests of nominal confidence interval coverage for causal parameters estimated by machine learning". https://arxiv.org/abs/2006.09613

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