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The task of addressing study\u2010specific heterogeneities is one of the most difficult challenges in synthesizing data from disparate sources. Bayesian hierarchical models with nonparametric extension provide a powerful and convenient platform that formalizes the information borrowing strength across the sources. In this paper, we propose a propensity score\u2010based Bayesian nonparametric Dirichlet process mixture model that summarizes subject\u2010level information from randomized and registry studies to draw inference on the causal treatment effect. Simulation studies are conducted to evaluate the model performance under different scenarios. 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