Before revision:  This paper investigates the possibility of identification of optimal dynamic regimes when data are generated from sequential (natural) experiments. We propose a framework in which we can partially learn the optimal dynamic regime and ordering of welfares, relaxing sequential randomization assumptions commonly employed in the literature .
After revision:  We introduce a framework in which we can partially learn the optimal dynamic regime and ordering of welfares, relaxing sequential randomization assumptions commonly employed in the literature .