Causal Inference of Ordinal Outcomes: A Bayesian Solution

Randomized experiments with ordinal outcomes are common in many scientific applications, but conventional causal estimands such as the average treatment effect are difficult to interpret because ordinal categories lack meaningful numerical spacing. We develop a Bayesian latent variable framework for