title: A general approach for predicting the behavior of the Supreme Court of the United States.
abstract: Building on developments in machine learning and prior work in the science of judicial prediction, we construct a model designed to predict the behavior of the Supreme Court of the United States in a generalized, out-of-sample context. To do so, we develop a time-evolving random forest classifier that leverages unique feature engineering to predict more than 240,000 justice votes and 28,000 cases outcomes over nearly two centuries (1816-2015). Using only data available prior to decision, our model outperforms null (baseline) models at both the justice and case level under both parametric and non-parametric tests. Over nearly two centuries, we achieve 70.2% accuracy at the case outcome level and 71.9% at the justice vote level. More recently, over the past century, we outperform an in-sample optimized null model by nearly 5%. Our performance is consistent with, and improves on the general level of prediction demonstrated by prior work; however, our model is distinctive because it can be applied out-of-sample to the entire past and future of the Court, not a single term. Our results represent an important advance for the science of quantitative legal prediction and portend a range of other potential applications.
authors.affiliation: Illinois Tech - Chicago-Kent College of Law, Chicago, IL, United States of America.|CodeX - The Stanford Center for Legal Informatics, Stanford, CA, United States of America.
authors.forename: Daniel Martin
authors.identifier: http://orcid.org/0000-0003-2720-0339
authors.initials: DM
authors.lastname: Katz
authors.affiliation: Illinois Tech - Chicago-Kent College of Law, Chicago, IL, United States of America.|CodeX - The Stanford Center for Legal Informatics, Stanford, CA, United States of America.
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Bommarito
authors.affiliation: South Texas College of Law Houston, Houston, TX, United States of America.
authors.forename: Josh
authors.initials: J
authors.lastname: Blackman
mesh_terms: D005544:Forecasting; D006801:Humans; D000069550:Machine Learning; D012935:Social Justice / Q000639:trends*; D035521:Supreme Court Decisions; D014481:United States