12 citations · 41 across the 11 of their papers we have counts for
14 papers · 1 filter
Posterior Predictive Propensity Scores and -Values
Peng Ding, Tianyu Guo
\citet{Rosenbaum83ps} introduced the notion of the propensity score and discussed its central role in causal inference with observational studies. Their paper, however, caused a fu…
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…
Model-assisted analyses of cluster-randomized experiments
Fangzhou Su, Peng Ding
Cluster-randomized experiments are widely used due to their logistical convenience and policy relevance. To analyze them properly, we must address the fact that the treatment is as…
Identification of Causal Effects Within Principal Strata Using Auxiliary Variables
Zhichao Jiang, Peng Ding
In causal inference, principal stratification is a framework for dealing with a posttreatment intermediate variable between a treatment and an outcome, in which the principal strat…
Sharp bounds on the relative treatment effect for ordinal outcomes
Jiannan Lu, Yunshu Zhang, Peng Ding
For ordinal outcomes, the average treatment effect is often ill-defined and hard to interpret. Echoing Agresti and Kateri (2017), we argue that the relative treatment effect can be…