Valid Simultaneous Inference in High-Dimensional Settings (with the hdm package for R)

Bach, Philipp; Chernozhukov, Victor; Spindler, Martin

doi:10.48550/arxiv.1809.04951

Cited by 4 publications

(6 citation statements)

References 5 publications

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“…Moreover, it is possible to derive an adjustment method for p values obtained from a test of multiple hypotheses, including classical adjustments such as the Bonferroni correction as well as the Romano-Wolf stepdown procedure (Romano and Wolf 2005a,b). The latter is implemented according to the algorithm for adjustment of p values as provided in Romano and Wolf (2016) and adapted to high-dimensional linear regression based on the lasso in Bach, Chernozhukov, and Spindler (2018).…”

Section: Remark 7: Computational Efficiencymentioning

confidence: 99%

DoubleML: An Object-Oriented Implementation of Double Machine Learning in R

Bach,

Kurz,

Chernozhukov

et al. 2024

J. Stat. Soft.

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The R package DoubleML implements the double/debiased machine learning framework of Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, Newey, and Robins (2018). It provides functionalities to estimate parameters in causal models based on machine learning methods. The double machine learning framework consists of three key ingredients: Neyman orthogonality, high-quality machine learning estimation and sample splitting. Estimation of nuisance components can be performed by various state-of-the-art machine learning methods that are available in the mlr3 ecosystem. DoubleML makes it possible to perform inference in a variety of causal models, including partially linear and interactive regression models and their extensions to instrumental variable estimation. The object-oriented implementation of DoubleML enables a high flexibility for the model specification and makes it easily extendable. This paper serves as an introduction to the double machine learning framework and the R package DoubleML. In reproducible code examples with simulated and real data sets, we demonstrate how DoubleML users can perform valid inference based on machine learning methods.

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Section: Remark 7: Computational Efficiencymentioning

confidence: 99%

DoubleML: An Object-Oriented Implementation of Double Machine Learning in R

Bach,

Kurz,

Chernozhukov

et al. 2024

J. Stat. Soft.

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show abstract

“…See Section 13.3 of [JWHT21] for more illustration of this dataset. library(boot); library(ISLR2) n <-nrow(Fund); y <-scale(Fund) m <-attr(y, "scaled:center"); s <-attr(y, "scaled:scale") tstat <-sqrt(n) * m/s; ord <-order(tstat) Finally, the R package hdm offers functionality on multiple hypothesis testing in high-dimensional approximately sparse linear regression models based upon Romano-Wolf step down procedures (see [BCS18] for documentation).…”

Section: 2mentioning

confidence: 99%

High-dimensional Data Bootstrap

Chernozhukov¹,

Chetverikov²,

Kato³

et al. 2022

Preprint

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This article reviews recent progress in high-dimensional bootstrap. We first review high-dimensional central limit theorems for distributions of sample mean vectors over the rectangles, bootstrap consistency results in high dimensions, and key techniques used to establish those results. We then review selected applications of high-dimensional bootstrap: construction of simultaneous confidence sets for high-dimensional vector parameters, multiple hypothesis testing via stepdown, post-selection inference, intersection bounds for partially identified parameters, and inference on best policies in policy evaluation. Finally, we also comment on a couple of future research directions.

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“…To verify the finite-sample performance of the implemented methods for simultaneous inference, we perform a small simulation study in a regression setup which is similar as the one used in Bach et al (2018). We would like to perform valid simultaneous inference on the coefficients θ in the regression model .…”

Section: Simultaneous Inferencementioning

confidence: 99%

DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R

Bach¹,

Chernozhukov²,

Kurz³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

The R package DoubleML implements the double/debiased machine learning framework of . It provides functionalities to estimate parameters in causal models based on machine learning methods. The double machine learning framework consist of three key ingredients: Neyman orthogonality, high-quality machine learning estimation and sample splitting. Estimation of nuisance components can be performed by various state-of-the-art machine learning methods that are available in the mlr3 ecosystem. DoubleML makes it possible to perform inference in a variety of causal models, including partially linear and interactive regression models and their extensions to instrumental variable estimation. The object-oriented implementation of DoubleML enables a high flexibility for the model specification and makes it easily extendable. This paper serves as an introduction to the double machine learning framework and the R package DoubleML. In reproducible code examples with simulated and real data sets, we demonstrate how DoubleML users can perform valid inference based on machine learning methods.

show abstract

Valid Simultaneous Inference in High-Dimensional Settings (with the hdm package for R)

Cited by 4 publications

References 5 publications

DoubleML: An Object-Oriented Implementation of Double Machine Learning in R

DoubleML: An Object-Oriented Implementation of Double Machine Learning in R

High-dimensional Data Bootstrap

DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R

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