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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.
Classification and regression trees
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Ideological values and the votes of US Supreme Court justices revisited,
Segal JA, Epstein L, Cameron CM, Spaeth HJ · 1995
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The influence of stare decisis on the votes of united states supreme court justices
Segal JA, Spaeth HJ · 1996
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Separation-of-powers games in the positive theory of congress and courts
Segal JA · 1997
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Calderia GA, Zorn C
1998
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Saad D. (1999)
1999
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Random forests
Breiman L · 2001
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The Supreme Court and the attitudinal model revisited
Segal JA, Spaeth HJ (2002) · 2002
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Dynamic ideal point estimation via markov chain monte carlo for the US Supreme Court, 1953-1999
Martin AD, Quinn KM · 2002
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Competing approaches to predicting supreme court decision making
Martin AD, Quinn, KM, Ruger, TW, Kim PT · 2004
Earlier work this paper cites.
The supreme court forecasting project: Legal and political science approaches to predicting supreme court decisionmaking
Ruger TW, Kim PT, Martin AD, Quinn KM · 2004
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An empirical comparison of supervised learning algorithms
Caruana R, Niculescu-Mizil A · 2006
Earlier work this paper cites.
Ideological drift among supreme court justices: Who, when, and how important
Epstein L, Martin AD, Quinn KM, Segal JA · 2007
Earlier work this paper cites.
Assessing preference change on the us supreme court
Martin AD, Quinn KM · 2007
Earlier work this paper cites.
Leicht EA, Clarkson G, Shedden K, Newman MEJ
2007
Cited alongside, same era.
Does legal doctrine matter? unpacking law and policy preferences on the us supreme court
Bailey MA, Maltzman, F · 2008
Cited alongside, same era.
Coding complexity: Bringing law to the empirical analysis of the supreme court
Shapiro C · 2008
Cited alongside, same era.
Automatically Classifying Case Texts and Predicting Outcomes
Ashley KD, Brüninghaus S · 2009
Cited alongside, same era.
Ho DE, Quinn KM
2010
Cited alongside, same era.
Supreme Court Reversal Rates: Evaluating the Federal Courts of Appeals
Hofer RE · 2010
Cited alongside, same era.
Quantitative legal prediction – or – how i learned to stop worrying and start preparing for the data driven future of the legal services industry
Katz DM · 2013
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The Law Machine
Harbert T · 2013
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Predicting the Behavior of the Supreme Court of the United States: A General Approach
Katz DM, Bommarito MJ, Blackman J · 2014
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Understanding Random Forests: From Theory to Practice
Louppe G · 2014
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Evaluation of machine-learning protocols for technology-assisted review in electronic discovery
Cormack GV, Grossman MR · 2014
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Machine learning and law
Surden H · 2014
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Guimerà R., Sales-Pardo M · 2011
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Shalev-Shwartz S
2011
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Casillas CJ, Enns PK, Wohlfarth PC
2011
Cited alongside, same era.
Scikit-learn: Machine learning in Python
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay, E · 2011
Cited alongside, same era.
Standing the test of time: The breadth of majority coalitions and the fate of us supreme court precedents
Benjamin SM, Desmarais BA · 2012
Cited alongside, same era.
Predicting securities fraud settlements and amounts: a hierarchical Bayesian model of federal securities class action lawsuits
McShane BB, Watson OP, Baker T, Griffith SJ · 2012
Cited alongside, same era.
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Choice of law: an empirical analysis
Sanga S · 2014
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Statistical Mechanics of the US Supreme Court, Journal of Statistical Physics, 2015; 160(2): 275-301
Lee ED, Broedersz CP, Bialek W · 2015
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Chollet F
2015
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Law on the Market? Evaluating the Securities Market Impact of Supreme Court Decisions
Katz DM, Bommarito MJ, Soellinger T, Chen JM · 2015
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Spaeth HJ, Epstein L, Martin AD, Segal JA, Ruger TJ, Benesh SC
2016
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Predicting judicial decisions of the European Court of Human Rights: A natural language processing perspective
Aletras N, Tsarapatsanis D, Preoţiuc-Pietro D, Lampos, V · 2016
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The Three Forms of (Legal) Prediction - Experts, Crowds and Algorithms
Katz DM, Bommarito MJ · 2016
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