What predicts the behavior of Justices on the U.S. Supreme Court? Previous attempts to develop predictive models of Supreme Court behavior leave room for improvement, in large part because of challenges involved with incorporating all available case-specific factors. In this article, we address these issues using an AdaBoost decision tree regressor, a popular approach in machine learning that is relatively underused in political science. We couple this approach with a novel mixed data set of both oral arguments data as well as data on case-level attributes. As we show, our AdaBoosted approach substantially outperforms not only existing predictive models of Supreme Court outcomes but also the predictions of legal experts. Substantively, this suggests that combining both legal information and the information revealed by the Justices themselves in the months leading to the decision provide the best information. We conclude the article by discussing possible applications of the AdaBoost approach within the social sciences.
Supreme Court justices employ law clerks to help them perform their duties. We study whether these clerks influence how justices vote in the cases they hear. We exploit the timing of the clerkship hiring process to link variation in clerk ideology to variation in judicial voting. To measure clerk ideology, we match clerks to the universe of disclosed political donations. We find that clerks influence judicial voting, especially in cases that are high-profile, legally significant, or when justices are more evenly divided. We interpret these results to suggest that clerk influence occurs through persuasion rather than delegation of decision-making authority.