Comment: Performance of Double-Robust Estimators When ``Inverse Probability'' Weights Are Highly Variable
James Robins, Mariela Sued, Quanhong Lei-Gomez, Andrea Rotnitzky
TL;DR
This paper comments on the challenges faced by double-robust estimators when inverse probability weights exhibit high variability, highlighting potential issues in their performance.
Contribution
It provides a critical analysis of the limitations of double-robust estimators under high variability of inverse probability weights.
Findings
High variability in weights can impair estimator performance.
Double-robust estimators may be biased or inefficient under certain conditions.
The comment suggests possible improvements or considerations for practitioners.
Abstract
Comment on ``Performance of Double-Robust Estimators When ``Inverse Probability'' Weights Are Highly Variable'' [arXiv:0804.2958]
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