Discussion of “On Nearly Assumption-Free Tests of Nominal Confidence Interval Coverage for Causal Parameters Estimated by Machine Learning”

@article{Kennedy2020DiscussionO,
  title={Discussion of “On Nearly Assumption-Free Tests of Nominal Confidence Interval Coverage for Causal Parameters Estimated by Machine Learning”},
  author={Edward H. Kennedy and Sivaraman Balakrishnan and Larry A. Wasserman},
  journal={Statistical Science},
  year={2020},
  volume={35},
  pages={540-544}
}
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… 
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On Nearly Assumption-Free Tests of Nominal Confidence Interval Coverage for Causal Parameters Estimated by Machine Learning
For many causal effect parameters of interest, doubly robust machine learning (DRML) estimators ψ^1 are the state-of-the-art, incorporating the good prediction performance of machine learning; the

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