High-dimensional inference for dynamic treatment effects-Reference-Cited by-同舟云学术

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High-dimensional inference for dynamic treatment effects

Published:2024-04-01 Issue:2 Volume:52 Page:
ISSN:0090-5364
Container-title:The Annals of Statistics
language:
Short-container-title:Ann. Statist.

Author:

Bradic Jelena¹,Ji Weijie²,Zhang Yuqian³

Affiliation:

1. Department of Mathematics and Halicioglu Data Science Institute, University of California San Diego

2. School of Statistics and Management, Shanghai University of Finance and Economics

3. Institute of Statistics and Big Data, Renmin University of China

Publisher

Institute of Mathematical Statistics

Reference50 articles.

1. 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.

2. BANG, H. and ROBINS, J. M. (2005). Doubly robust estimation in missing data and causal inference models. Biometrics 61 962–973.

3. DUKES, O. and VANSTEELANDT, S. (2021). Inference for treatment effect parameters in potentially misspecified high-dimensional models. Biometrika 108 321–334.

4. ROBINS, J. M. (2000a). Marginal structural models versus structural nested models as tools for causal inference. In Statistical Models in Epidemiology, the Environment, and Clinical Trials (Minneapolis, MN, 1997). IMA Vol. Math. Appl. 116 95–133. Springer, New York.

5. VAN DER LAAN, M. J. and GRUBER, S. (2012). Targeted minimum loss based estimation of causal effects of multiple time point interventions. Int. J. Biostat. 8 Art. 9, 41.

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