143 citations · 190 across the 13 of their papers we have counts for
9 papers · 1 filter
Hypothesizing an effect size by considering individual variation
Andrew Gelman, Amy Krefman, Lauren Kennedy +1
When designing and evaluating an experiment or observational study, it is useful to have a realistic hypothesis regarding the average treatment effect. We present an approach to co…
When Bayes goes bad: Weakly-regularized covariate adjustment leads to a biased estimate of prevalence
Swen Kuh, Lauren Kennedy, Qixuan Chen +1
When estimating population prevalence from a non-random sample, it is important to adjust for differences between sample and population. However, adjustment for multiple factors re…
Improving Survey Inference in Two-phase Designs Using Bayesian Machine Learning
Xinru Wang, Anyu Zhu, Lauren Kennedy +2
The two-phase sampling design is a cost-effective strategy widely used in public health research. Analyzing the Phase II sample often involves creating subsample-specific weights.…
Causal quartets: Different ways to attain the same average treatment effect
Andrew Gelman, Jessica Hullman, Lauren Kennedy
The average causal effect can often be best understood in the context of its variation. We demonstrate with two sets of four graphs, all of which represent the same average effect…
Cross-validatory model selection for Bayesian autoregressions with exogenous regressors
Alex Cooper, Dan Simpson, Lauren Kennedy +2
Bayesian cross-validation (CV) is a popular method for predictive model assessment that is simple to implement and broadly applicable. A wide range of CV schemes is available for t…
Using leave-one-out cross-validation (LOO) in a multilevel regression and poststratification (MRP) workflow: A cautionary tale
Swen Kuh, Lauren Kennedy, Qixuan Chen +1
In recent decades, multilevel regression and poststratification (MRP) has surged in popularity for population inference. However, the validity of the estimates can depend on detail…
Treatment effect estimation with Multilevel Regression and Poststratification
Yuxiang Gao, Lauren Kennedy, Daniel Simpson
Multilevel regression and poststratification (MRP) is a flexible modeling technique that has been used in a broad range of small-area estimation problems. Traditionally, MRP studie…
Bayesian Workflow
Andrew Gelman, Aki Vehtari, Daniel Simpson +7
The Bayesian approach to data analysis provides a powerful way to handle uncertainty in all observations, model parameters, and model structure using probability theory. Probabilis…
Improving multilevel regression and poststratification with structured priors
Yuxiang Gao, Lauren Kennedy, Daniel Simpson +1
A central theme in the field of survey statistics is estimating population-level quantities through data coming from potentially non-representative samples of the population. Multi…