Principal stratification is a statistical technique used in causal inference when adjusting results for post-treatment covariates.
It is a generalization of the local average treatment effect (LATE) in the sense of presenting applications besides all-or-none compliance.
The LATE method, which was independently developed by Imbens and Angrist (1994)[1] and Baker and Lindeman (1994)[2] also included the key exclusion restriction and monotonicity assumptions for identifiability.
With a binary post-treatment covariate (e.g. attrition) and a binary treatment (e.g. "treatment" and "control") there are four possible strata in which subjects could be: If the researcher knew the stratum for each subject then the researcher could compare outcomes only within the first stratum and estimate a valid causal effect for that population.
In applied evaluation research, principal strata are commonly referred to as "endogenous" strata or "subgroups" and involve specialized methods of analysis for examining the effects of interventions or treatments in the medical and social sciences.