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Structure-agnostic causal inference studies how well one can estimate a treatment effect given black-box machine learning estimates of nuisance functions (like the impact of confounders on treatment and outcomes).
Optimal global rates of convergence for nonparametric regression
Charles J Stone · 1982
Earlier work this paper cites.
Root-n-consistent semiparametric regression
Peter M Robinson · 1988
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Robert Tibshirani · 1996
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Improved rates and asymptotic normality for nonparametric neural network estimators
Xiaohong Chen and Halbert White · 1999
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Saso Džeroski and Bernard Ženko · 2004
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Causal inference with general treatment regimes: Generalizing the propensity score
Kosuke Imai and David A Van Dyk · 2004
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Regularization and variable selection via the elastic net
Hui Zou and Trevor Hastie · 2005
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Consistency of random forests and other averaging classifiers
Gérard Biau, Luc Devroye, and Gäbor Lugosi · 2008
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Introduction to nonparametric estimation
Alexandre B Tsybakov · 2008
Earlier work this paper cites.
Effect of mean on variance function estimation in nonparametric regression
Lie Wang, Lawrence D Brown, T Tony Cai, and Michael Levine · 2008
Earlier work this paper cites.
Semiparametric minimax rates
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Inference for high-dimensional sparse econometric models
Alexandre Belloni, Victor Chernozhukov, and Christian Hansen · 2011
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Testing composite hypotheses, hermite polynomials and optimal estimation of a nonsmooth functional
T Tony Cai and Mark G Low · 2011
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Limit theorems for large deviations , volume 73
Leonas Saulis and VA Statulevicius · 2012
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Pivotal estimation via square-root lasso in nonparametric regression
Alexandre Belloni, Victor Chernozhukov, and Lie Wang · 2014
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Higher-order Airy functions of the first kind and spectral properties of the massless relativistic quartic anharmonic oscillator
High-dimensional probability: An introduction with applications in data science , volume 47
Roman Vershynin · 2018
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Probability: theory and examples , volume 49
Rick Durrett · 2019
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Nonparametric regression using deep neural networks with relu activation function
Anselm Johannes Schmidt-Hieber · 2020
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Estimation and inference with trees and forests in high dimensions
Vasilis Syrgkanis and Manolis Zampetakis · 2020
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Propensity score-based methods for causal inference in observational studies with non-binary treatments
Shandong Zhao, David A van Dyk, and Kosuke Imai · 2020
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Deep neural networks for estimation and inference
Max H Farrell, Tengyuan Liang, and Sanjog Misra · 2021
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Samuel O Durugo · 2014
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Statistical learning with sparsity
Trevor Hastie, Robert Tibshirani, and Martin Wainwright · 2015
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The gauss-airy functions and their properties
Alireza Ansari · 2016
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Double/debiased machine learning for treatment and structural parameters: Double/debiased machine learning
Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey, and James Robins · 2018
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Orthogonal machine learning: Power and limitations
Lester Mackey, Vasilis Syrgkanis, and Ilias Zadik · 2018
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Mémoire sur les intégrales définies eulériennes et sur leur application à la théorie des suites, ainsi qu’à l’évaluation des fonctions des grands nombres
Jacques Binet
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Automatic debiased machine learning of causal and structural effects
Victor Chernozhukov, Whitney K Newey, and Rahul Singh · 2022
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The fundamental limits of structure-agnostic functional estimation
Sivaraman Balakrishnan, Edward H Kennedy, and Larry Wasserman · 2023
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A neyman-orthogonalization approach to the incidental parameter problem
Stéphane Bonhomme, Koen Jochmans, and Martin Weidner · 2024
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Structure-agnostic optimality of doubly robust learning for treatment effect estimation
Jikai Jin and Vasilis Syrgkanis · 2024
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