  With reference to a single mediator context, this brief report presents a
model-based strategy to estimate counterfactual direct and indirect effects
when the response variable is ordinal and the mediator is binary. Postulating a
logistic regression model for the mediator and a cumulative logit model for the
outcome, the exact parametric formulation of the causal effects is presented,
thereby extending previous work that only contained approximated results. The
identification conditions are equivalent to the ones already established in the
literature. The effects can be estimated by making use of standard statistical
software and standard errors can be computed via a bootstrap algorithm. To make
the methodology accessible, routines to implement the proposal in R are
presented in the Appendix. A natural effect model coherent with the postulated
data generating mechanism is also derived.
