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6 papers · 2 filters
Difference-in-Differences for Ordinal Outcomes: Application to the Effect of Mass Shootings on Attitudes toward Gun Control
Soichiro Yamauchi
The difference-in-differences (DID) design is widely used in observational studies to estimate the causal effect of a treatment when repeated observations over time are available.…
Supervised Robust Profile Clustering
Briana Stephenson, Amy Herring, Andrew Olshan
In many studies, dimension reduction methods are used to profile participant characteristics. For example, nutrition epidemiologists often use latent class models to characterize d…
The Essential Role of Empirical Validation in Legislative Redistricting Simulation
Benjamin Fifield, Kosuke Imai, Jun Kawahara +1
As granular data about elections and voters become available, redistricting simulation methods are playing an increasingly important role when legislatures adopt redistricting plan…
Individual-level Modeling of COVID-19 Epidemic Risk
Andres Colubri, Kailash Yadav, Abhishek Jha +1
The ongoing COVID-19 pandemic calls for a multi-faceted public health response comprising complementary interventions to control the spread of the disease while vaccines and therap…
Pixelate to communicate: visualising uncertainty in maps of disease risk and other spatial continua
Aimee R Taylor, James A Watson, Caroline O Buckee
Maps have long been been used to visualise estimates of spatial variables, in particular disease burden and risk. Predictions made using a geostatistical model have uncertainty tha…
Mendelian randomization and causal networks for systematic analysis of omics
Azam Yazdani
Mendelian randomization implemented through instrumental variable analysis is frequently discussed in causality and recently the number of applications on real data is increasing.…