Document de travail

What Matters: Agreement Between U.S. Courts of Appeals Judges

Daniel L. Chen, Xing Cui, Lanyu Shang et Junchao Zheng

Résumé

Federal courts are a mainstay of the justice system in the United States. In this study, we analyze 387,898 cases from U.S. Courts of Appeals, where judges are randomly assigned to panels of three. We predict which judge dissents against co-panelists and analyze the dominant features that predict such dissent with a particular attention to the biographical features that judges share. Random forest, a method developed in Breiman (2001), achieves the best classification. Dissent is predominantly driven by case features, though personal features also predict agreement.

Remplacé par

Daniel L. Chen, Xing Cui, Lanyu Shang et Junchao Zheng, « What Matters: Agreement Between U.S. Courts of Appeals Judges », Journal of Machine Learning Research, 2016.

Voir aussi

Publié dans

TSE Working Paper, n° 16-747, décembre 2016