Social distancing and COVID-19: Randomization inference for a structured dose-response relationship-Reference-Cited by-同舟云学术

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Social distancing and COVID-19: Randomization inference for a structured dose-response relationship

Published:2023-03-01 Issue:1 Volume:17 Page:
ISSN:1932-6157
Container-title:The Annals of Applied Statistics
language:
Short-container-title:Ann. Appl. Stat.

Author:

Zhang Bo¹,Heng Siyu²,Ye Ting³,Small Dylan S.¹

Affiliation:

1. Department of Statistics and Data Science, The Wharton School, University of Pennsylvania

2. Graduate Group in Applied Mathematics and Computational Science, School of Arts and Sciences, University of Pennsylvania

3. Department of Biostatistics, University of Washington

Publisher

Institute of Mathematical Statistics

Subject

Statistics, Probability and Uncertainty,Modeling and Simulation,Statistics and Probability

Reference57 articles.

1. Rubin, D. B. (2005). Causal inference using potential outcomes: Design, modeling, decisions. J. Amer. Statist. Assoc. 100 322–331.

2. RUBIN, D. B. (2007). The design versus the analysis of observational studies for causal effects: Parallels with the design of randomized trials. Stat. Med. 26 20–36.

3. ROSENBAUM, P. R. (2010). Design of Observational Studies. Springer Series in Statistics. Springer, New York.

4. BERGER, R. L. and BOOS, D. D. (1994). P values maximized over a confidence set for the nuisance parameter. J. Amer. Statist. Assoc. 89 1012–1016.

5. BAIOCCHI, M., SMALL, D. S., LORCH, S. and ROSENBAUM, P. R. (2010). Building a stronger instrument in an observational study of perinatal care for premature infants. J. Amer. Statist. Assoc. 105 1285–1296.

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