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

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

zbMATH Keywords

causal inferencelongitudinal studiesstatistical matchingCOVID-19randomization inferencedose-response relationship

Mathematics Subject Classification ID

Applications of statistics (62Pxx)

Uses Software

• nbpMatching
• GitHub

Cites Work

• Unnamed Item
• 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

This page was built for publication: Social distancing and COVID-19: randomization inference for a structured dose-response relationship