The Costs and Benefits of Uniformly Valid Causal Inference with High-Dimensional Nuisance Parameters-Reference-Cited by-同舟云学术

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The Costs and Benefits of Uniformly Valid Causal Inference with High-Dimensional Nuisance Parameters

Published:2023-02-01 Issue:1 Volume:38 Page:
ISSN:0883-4237
Container-title:Statistical Science
language:
Short-container-title:Statist. Sci.

Author:

Moosavi Niloofar¹,Häggström Jenny²,de Luna Xavier³

Affiliation:

1. Niloofar Moosavi is Ph.D. Student, Department of Statistics, USBE, Umeå University, 901 87, Umeå, Sweden

2. Jenny Häggström is Associate Professor, Department of Statistics, USBE, Umeå University, 901 87, Umeå, Sweden

3. Xavier de Luna is Professor, Department of Statistics, USBE, Umeå University, 901 87, Umeå, Sweden

Publisher

Institute of Mathematical Statistics

Subject

Statistics, Probability and Uncertainty,General Mathematics,Statistics and Probability

Reference62 articles.

1. R Core Team (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.

2. Athey, S., Imbens, G. W. and Wager, S. (2018). Approximate residual balancing: Debiased inference of average treatment effects in high dimensions. J. R. Stat. Soc. Ser. B. Stat. Methodol. 80 597–623.

3. D’Amour, A., Ding, P., Feller, A., Lei, L. and Sekhon, J. (2021). Overlap in observational studies with high-dimensional covariates. J. Econometrics 221 644–654.

4. Zheng, W. and van der Laan, M. J. (2011). Cross-validated targeted minimum-loss-based estimation. In Targeted Learning. Springer Ser. Statist. 459–474. Springer, New York.

5. AVAGYAN, V. and VANSTEELANDT, S. (2021). High-dimensional inference for the average treatment effect under model misspecification using penalized bias-reduced double-robust estimation. Biostatistics & Epidemiology 1–18.

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