A large empirical literature examines how judges' traits affect how cases get resolved. This literature has led many to conclude that judges matter for case outcomes. But how much do they matter? Existing empirical findings understate the true extent of judicial influence over case outcomes since standard estimation techniques hide some disagreement among judges. We devise a novel machine learning method to reveal additional sources of disagreement. Applying this method to the Ninth Circuit, we estimate that at least 28% of cases could be decided differently based solely on the panel they were assigned to.
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