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…

References

SHOWING 1-10 OF 14 REFERENCES
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…
Universal inference
TLDR
A surprisingly simple method for producing statistical significance statements without any regularity conditions and it is shown that in settings when computing the MLE is hard, for the purpose of constructing valid tests and intervals, it is sufficient to upper bound the maximum likelihood.
Cross-fitting and fast remainder rates for semiparametric estimation
There are many interesting and widely used estimators of a functional with ?nite semi-parametric variance bound that depend on nonparametric estimators of nuisance func-tions. We use cross-?tting to…
Minimax estimation of a functional on a structured high-dimensional model
We introduce a new method of estimation of parameters in semi-parametric and nonparametric models. The method is based on estimating equations that are U-statistics in the observations. The…
Semiparametric Minimax Rates.
TLDR
The approach of [1], which is based on comparing a "true distribution" to a convex mixture of perturbed distributions to a comparison of two convex mixtures, is extended and applied to two examples of semiparametric functionals: the estimation of a mean response when response data are missing at random, andThe estimation of an expected conditional covariance functional.
Effect of mean on variance function estimation in nonparametric regression
Variance function estimation in nonparametric regression is considered and the minimax rate of convergence is derived. We are particularly interested in the effect of the unknown mean on the…
Higher order influence functions and minimax estimation of nonlinear functionals
We present a theory of point and interval estimation for nonlinear functionals in parametric, semi-, and non-parametric models based on higher order influence functions (Robins (2004), Section 9; Li…
Estimation of Integral Functionals of a Density
Let φ be a smooth function of k + 2 variables. We shall investigate in this paper the rates of convergence of estimators of T(f)= fφ(f(x), f'(x),..., f (k) (x), x) dx when f belongs to some class of…
Wavelet Shrinkage: Asymptopia?
TLDR
A method for curve estimation based on n noisy data: translate the empirical wavelet coefficients towards the origin by an amount √(2 log n) /√n and draw loose parallels with near optimality in robustness and also with the broad near eigenfunction properties of wavelets themselves.
Efficient estimation of integral functionals of a density
TLDR
This paper proposes an alternative and somehow simpler method of estimation based on orthogonal projections of the density based on Bickel and Ritov's estimator.
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