Is the treatment beneficial? Causal Inference with Ordinal Outcomes: A Bayesian Solution
处理是否有益?有序结局因果推断的贝叶斯方法
Randomized experiments with ordinal outcomes are common across scientific applications, but conventional causal estimands such as the average treatment effect are difficult to interpret because ordinal categories lack meaningful numerical spacing. Two interpretable estimands, the probabilities that the treatment is beneficial and strictly beneficial, depend on the joint distribution of the potential outcomes and are therefore not identifiable from observed data. The sharp…