After considering the case, each participating Justice ultimately casts his or her vote on whether to affirm or reverse the status quo (typically seen through the lens of a decision by the lower court or special master). If that petition is granted, the parties then submit written materials supporting their position and later provide oral argument before the Court. In most situations, the Court decides to hear a case by granting a petition for a writ of certiorari. Thousands of petitioners annually appeal their cases to the Supreme Court. Luckily, the Court provides a new opportunity to test each year. To the extent that scholars in both disciplines (social science and law) seek to explain court behavior, they ought to test their theories not only against cases already decided, but against future outcomes as well.” As noted in, “the best test of an explanatory theory is its ability to predict future events. When models are formalized, they are typically assessed ex post to infer causes, rather than used ex ante to predict future cases. Not only are these models not backtested historically, but many are difficult to formalize or reproduce at all. Will the Justices vote based on the political preferences of the President who appointed them or form a coalition along other dimensions? Will the Court counter expectations with an unexpected ruling?ĭespite the multitude of pundits and vast human effort devoted to the task, the quality of the resulting predictions and the underlying models supporting most forecasts is unclear. Every year, newspapers, television and radio pundits, academic journals, law reviews, magazines, blogs, and tweets predict how the Court will rule in a particular case. Unsurprisingly, predicting the behavior of the Court is one of the great pastimes for legal and political observers. In many instances, the Court’s decisions are meaningful not just for the litigants per se, but for society as a whole. Each term brings with it a series of challenging, important cases that cover legal questions as diverse as tax law, freedom of speech, patent law, administrative law, equal protection, and environmental law. This does not alter our adherence to PLOS ONE policies on sharing data and materials.Īs the leaves begin to fall each October, the first Monday marks the beginning of another term for the Supreme Court of the United States. We received no financial contributions from LexPredict or anyone else for this paper. įunding: The author(s) received no specific funding for this work.Ĭompeting interests: All Authors are Members of a LexPredict, LLC which provides consulting services to various legal industry stakeholders. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: Data and replication code are available on Github at the following URL. Received: JanuAccepted: MaPublished: April 12, 2017Ĭopyright: © 2017 Katz et al. Our results represent an important advance for the science of quantitative legal prediction and portend a range of other potential applications.Ĭitation: Katz DM, Bommarito MJ II, Blackman J (2017) A general approach for predicting the behavior of the Supreme Court of the United States. 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. More recently, over the past century, we outperform an in-sample optimized null model by nearly 5%. Over nearly two centuries, we achieve 70.2% accuracy at the case outcome level and 71.9% at the justice vote level. 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. 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). 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.
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