Social distancing and COVID-19: randomization inference for a structured dose-response relationship
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Publication:2686015
DOI10.1214/22-AOAS1613OpenAlexW3103793373MaRDI QIDQ2686015
Bo Zhang, Siyu Heng, Ting Ye, Dylan S. Small
Publication date: 24 February 2023
Published in: The Annals of Applied Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2011.06917
causal inferencelongitudinal studiesstatistical matchingCOVID-19randomization inferencedose-response relationship
Uses Software
Cites Work
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- Randomization Inference for Treatment Effect Variation
- Matching methods for causal inference: a review and a look forward
- Causal impact of masks, policies, behavior on early Covid-19 pandemic in the U.S.
- Randomization-Based Inference Within Principal Strata
- Mathematical Models in Population Biology and Epidemiology
- P Values Maximized Over a Confidence Set for the Nuisance Parameter
- Matching With Doses in an Observational Study of a Media Campaign Against Drug Abuse
- Exactp-Values for Network Interference
- Bayesian Inference for Sequential Treatments Under Latent Sequential Ignorability
- Building a Stronger Instrument in an Observational Study of Perinatal Care for Premature Infants
- Causal Inference for Statistics, Social, and Biomedical Sciences
- Interference Between Units in Randomized Experiments
- Causal Inference Using Potential Outcomes
- Evaluating Kindergarten Retention Policy
- Design of observational studies
- Observational studies.
- Bridging preference‐based instrumental variable studies and cluster‐randomized encouragement experiments: Study design, noncompliance, and average cluster effect ratio
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