Doubly robust tests of exposure effects under high‐dimensional confounding-Reference-Cited by-同舟云学术

Doubly robust tests of exposure effects under high‐dimensional confounding

Published:2020-02-28 Issue:4 Volume:76 Page:1190-1200
ISSN:0006-341X
Container-title:Biometrics
language:en
Short-container-title:Biometrics

Author:

Dukes Oliver¹^ORCID,Avagyan Vahe²^ORCID,Vansteelandt Stijn¹³^ORCID

Affiliation:

1. Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent Belgium

2. Mathematical and Statistical Methods Group Wageningen University and Research Wageningen The Netherlands

3. Department of Medical Statistics London School of Hygiene and Tropical Medicine London United Kingdom

Funder

Bijzonder Onderzoeksfonds

Fonds Wetenschappelijk Onderzoek

Publisher

Wiley

Subject

Applied Mathematics,General Agricultural and Biological Sciences,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Medicine,Statistics and Probability

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/biom.13231

Reference23 articles.

1. Avagyan V.andVansteelandt S.(2017)Honest data‐adaptive inference for the average treatment effect under model misspecification using penalised bias‐reduced double‐robust estimation. Preprint 2017 arXiv:1708.03787.

2. Inference on Treatment Effects after Selection among High-Dimensional Controls

3. Post-Selection Inference for Generalized Linear Models With Many Controls

4. Double/debiased machine learning for treatment and structural parameters

5. hdm: High-Dimensional Metrics

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