Multiway Cluster Robust Double/Debiased Machine Learning

Chiang, Harold D.; Kato, Kengo; Ma, Yanxue; Sasaki, Yuya

doi:10.1080/07350015.2021.1895815

Cited by 30 publications

(23 citation statements)

References 28 publications

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“…Our uniform convergence proofs build upon those of Hansen (2008). Nonparametric density estimation with dyadic data was first considered by Graham et al (2019); Chiang et al (2019) present uniform convergence results for dyadic density estimators. 1 Our results provide insight in the structure of dyadic nonparametric estimation problems.…”

Section: Introductionmentioning

confidence: 77%

“…They should, as has been true with their i.i.d. predecessors, also be useful for proving consistency of two-step semiparametric M-estimators under dyadic dependence (see Chiang et al (2019) for some results on double machine learning with dyadic data).…”

Section: Introductionmentioning

confidence: 99%

“…It is possible that the methods of inference presented inChiang et al (2019) could be adapted to our setting.…”

mentioning

confidence: 99%

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Minimax Risk and Uniform Convergence Rates for Nonparametric Dyadic Regression

2021

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We thank seminar audiences at University of California -Berkeley for helpful feedback. We also thank Matias Cattaneo, Michael Jansson and Harold Chiang for useful comments and discussion. All the usual disclaimers apply. Financial support from the National Science Foundation (SES #1357499, SES #1851647) is gratefully acknowledged. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

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Section: Introductionmentioning

confidence: 77%

Section: Introductionmentioning

confidence: 99%

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Minimax Risk and Uniform Convergence Rates for Nonparametric Dyadic Regression

2021

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“…by (19). Due to Weyl's inequality and Slutsky's theorem, (32), (33), and (37), Proof of Lemma I. 19.…”

Section: Definition I18 We Denote By a I K The Row-wise Concatenation Of The Observationsmentioning

confidence: 94%

“…Lewis and Syrgkanis [60] extend DML to estimate dynamic effects of treatments. Chiang et al [32] apply DML under multiway clustered sampling environments. Cui and Tchetgen Tchetgen [36] propose a technique to reduce the bias of DML estimators.…”

Section: Additional Literaturementioning

confidence: 99%

Regularizing double machine learning in partially linear endogenous models

Emmenegger

Bühlmann

2021

Electron. J. Statist.

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The linear coefficient in a partially linear model with confounding variables can be estimated using double machine learning (DML). However, this DML estimator has a two-stage least squares (TSLS) interpretation and may produce overly wide confidence intervals. To address this issue, we propose a regularization and selection scheme, regsDML, which leads to narrower confidence intervals. It selects either the TSLS DML estimator or a regularization-only estimator depending on whose estimated variance is smaller. The regularization-only estimator is tailored to have a low mean squared error. The regsDML estimator is fully data driven. The regsDML estimator converges at the parametric rate, is asymptotically Gaussian distributed, and asymptotically equivalent to the TSLS DML estimator, but regsDML exhibits substantially better finite sample properties. The regsDML estimator uses the idea of k-class estimators, and we show how DML and k-class estimation can be combined to estimate the linear coefficient in a partially linear endogenous model. Empirical examples demonstrate our methodological and theoretical developments. Software code for our regsDML method is available in the R-package dmlalg.

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Fast and reliable jackknife and bootstrap methods for cluster‐robust inference

MacKinnon

Nielsen

Webb

2023

J of Applied Econometrics

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We provide computationally attractive methods to obtain jackknife-based cluster-robust variance matrix estimators (CRVEs) for linear regression models estimated by least squares. We also propose several new variants of the wild cluster bootstrap, which involve these CRVEs, jackknife-based bootstrap data-generating processes, or both. Extensive simulation experiments suggest that the new methods can provide much more reliable inferences than existing ones in cases where the latter are not trustworthy, such as when the number of clusters is small and/or cluster sizes vary substantially. Three empirical examples illustrate the new methods.

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Multiway Cluster Robust Double/Debiased Machine Learning

Cited by 30 publications

References 28 publications

Minimax Risk and Uniform Convergence Rates for Nonparametric Dyadic Regression

Minimax Risk and Uniform Convergence Rates for Nonparametric Dyadic Regression

Regularizing double machine learning in partially linear endogenous models

Fast and reliable jackknife and bootstrap methods for cluster‐robust inference

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