  Difference in differences (DD) is widely used to find policy/treatment
effects with observational data, but applying DD to limited dependent variables
(LDV's) Y has been problematic. This paper addresses how to apply DD and
related approaches (such as "ratio in ratios" or "ratio in odds ratios") to
binary, count, fractional, multinomial or zero-censored Y under the unifying
framework of `generalized linear models with link functions'. We evaluate DD
and the related approaches with simulation and empirical studies, and recommend
'Poisson Quasi-MLE' for non-negative (such as count or zero-censored) Y and
(multinomial) logit MLE for binary, fractional or multinomial Y.
