ResearchStats: Jiannan Lu

Bounding causal effects for ordinal outcomes: partial identification and objective inference We consider the problem of causal inference for ordinal outcomes, in which the estimands are often unidentifiable. Instead of invoking strong modeling assumptions, we take the objective approach of partial identification and derive closed form expressions for the sharp bounds of various estimands. Our results are assumption free, scientifically meaningful and extremely easy to compute and interpret. We illustrate our results through simulation studies and a real data example.