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Circuit courts in the United States come to conflicting decisions on the same legal question, creating federal law that varies by geography. While the consequences of non-uniformity are well studied, the process that generates these divergent decisions is not. In this paper, I develop a model of sequential judicial decision-making where courts learn about and rule on new legal issues. Using an original data set of decisions on issues of “first impression,” I test predictions from the model. I find that the effect of ideological distance between courts depends on whether previous decisions are consistent with known court biases. Bias-consistent decisions result in an ally principle, where ideological distance increases the likelihood of a circuit split. However, when courts make bias-inconsistent decisions, ideological distance increases the chances of legal uniformity. I disentangle this effect into both a private and public learning mechanism. My analysis provides both a theoretical and empirical foundation for understanding when we should expect circuit splits to occur.