10 citations · 28 across the 16 of their papers we have counts for
5 papers · 1 filter
Covariate adjustment in multi-armed, possibly factorial experiments
Anqi Zhao, Peng Ding
Randomized experiments are the gold standard for causal inference, and justify simple comparisons across treatment groups. Regression adjustment provides a convenient way to incorp…
No star is good news: A unified look at rerandomization based on -values from covariate balance tests
Anqi Zhao, Peng Ding
Modern social and biomedical scientific publications require the reporting of covariate balance tables with not only covariate means by treatment group but also the associated -…
To adjust or not to adjust? Estimating the average treatment effect in randomized experiments with missing covariates
Anqi Zhao, Peng Ding
Complete randomization allows for consistent estimation of the average treatment effect based on the difference in means of the outcomes without strong modeling assumptions on the…
Reconciling design-based and model-based causal inferences for split-plot experiments
Anqi Zhao, Peng Ding
The split-plot design assigns different interventions at the whole-plot and sub-plot levels, respectively, and induces a group structure on the final treatment assignments. A commo…
Regression-based causal inference with factorial experiments: estimands, model specifications, and design-based properties
Anqi Zhao, Peng Ding
Factorial designs are widely used due to their ability to accommodate multiple factors simultaneously. The factor-based regression with main effects and some interactions is the do…