This paper specifies a general set of conditions under which the impacts of a policy can be identified using data generated under a different policy regime. We show that some of the policy impacts can be identified under relatively weak conditions on the data and structure of a model. Based on the identification results we develop estimators of policy impacts. We discuss a nonparametric method to implement the estimation but also discuss semiparametric methods in order to reduce the conditioning dimension. We then provide an empirical example of the impact of tuition subsidies using the ideas. While the framework used in this paper is fairly narrow, we believe this approach can be applied to a broad set of problems.