Abstract

https://ssrn.com/abstract=2463244
 


 



A General Approach for Predicting the Behavior of the Supreme Court of the United States


Daniel Martin Katz


Illinois Tech - Chicago Kent College of Law

Michael James Bommarito II


LexPredict, LLC; Bommarito Consulting, LLC; Chicago-Kent College of Law - Illinois Institute of Technology; Michigan State College of Law

Josh Blackman


South Texas College of Law Houston

January 16, 2017


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 which leverages some 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.

Number of Pages in PDF File: 18

Keywords: United States Supreme Court, Machine Learning, Law and Social Science, Quantitative Legal Prediction, SCOTUS prediction, artificial intelligence and law, online learning, judicial prediction, random forest

JEL Classification: C45, K40


Open PDF in Browser Download This Paper

Date posted: July 9, 2014 ; Last revised: January 19, 2017

Suggested Citation

Katz, Daniel Martin and Bommarito, Michael James and Blackman, Josh, A General Approach for Predicting the Behavior of the Supreme Court of the United States (January 16, 2017). Available at SSRN: https://ssrn.com/abstract=2463244 or http://dx.doi.org/10.2139/ssrn.2463244

Contact Information

Daniel Martin Katz (Contact Author)
Illinois Tech - Chicago Kent College of Law ( email )
565 W. Adams St.
Chicago, IL 60661-3691
United States
HOME PAGE: http://www.danielmartinkatz.com/

Michael James Bommarito II
LexPredict, LLC ( email )
MI
United States
HOME PAGE: http://lexpredict.com
Bommarito Consulting, LLC ( email )
MI 48098
United States
HOME PAGE: http://bommaritollc.com
Chicago-Kent College of Law - Illinois Institute of Technology ( email )
565 W. Adams St.
Chicago, IL 60661-3691
United States
Michigan State College of Law ( email )
318 Law College Building
East Lansing, MI 48824-1300
United States
Josh Blackman
South Texas College of Law Houston ( email )
1303 San Jacinto Street
Houston, TX 77002
United States
Feedback to SSRN


Paper statistics
Abstract Views: 12,804
Downloads: 2,559
Download Rank: 3,410