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

Chen, Daniel L., Cui, Xing, Shang, Lanyu and Zheng, Junchao (2019) What Matters: Agreement Between U.S. Courts of Appeals Judges. Journal of Machine Learning Research. (In Press)

This is the latest version of this item.

Full text not available from this repository.
Official URL: http://tse-fr.eu/pub/31291


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.

Item Type: Article
Language: English
Date: 2019
Refereed: Yes
Divisions: TSE-R (Toulouse)
Site: UT1
Date Deposited: 09 Jan 2017 13:41
Last Modified: 08 Oct 2019 23:03
["eprint_fieldname_oai_identifier" not defined]: oai:tse-fr.eu:31291
URI: http://publications.ut-capitole.fr/id/eprint/22636

Available Versions of this Item

Actions (login required)

View Item View Item