Details

Autor(en) / Beteiligte

• Katz, Daniel Martin
• Bommarito, 2nd, Michael J
• Blackman, Josh
• Amaral, Luís A. Nunes

Titel

A general approach for predicting the behavior of the Supreme Court of the United States

Ist Teil von

• PloS one, 2017-04, Vol.12 (4), p.e0174698-e0174698

Ort / Verlag

United States: Public Library of Science

Erscheinungsjahr

2017

Quelle

MEDLINE

Beschreibungen/Notizen

• 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.