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We derive novel results for the identification of counterfactuals in DDC models, such as non\u2010additive changes in payoffs or changes to agents' choice sets. In doing so, we propose a general framework that allows the investigation of the identification of a broad class of counterfactuals (covering virtually any counterfactual encountered in applied work). To illustrate the results, we consider a firm entry\/exit problem numerically, as well as an empirical model of agricultural land use. 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