  This paper develops a novel nonparametric identification method for treatment
effects in settings where individuals self-select into treatment sequences. I
propose an identification strategy which relies on a dynamic version of
standard Instrumental Variables (IV) assumptions and builds on a dynamic
version of the Marginal Treatment Effects (MTE) as the fundamental building
block for treatment effects. The main contribution of the paper is to relax
assumptions on the support of the observed variables and on unobservable gains
of treatment that are present in the dynamic treatment effects literature.
Monte Carlo simulation studies illustrate the desirable finite-sample
performance of a sieve estimator for MTEs and Average Treatment Effects (ATEs)
on a close-to-application simulation study.
