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

By Daniel Martin Katz, Michael James Bommarito II, Josh Blackman. PLoS ONE, 2016

425 citations recorded by the source corpus.

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.

Subfields: Law, Political Science and International Relations

View Paper: https://www.semanticscholar.org/paper/61b3b14977068904450bcba9a76c7d58bfc89ce8

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Citations in the corpus

Few-Shot Charge Prediction with Discriminative Legal Attributes (2018, background): “Katz et al. (2017) predict the US Supreme Court’s decisions with efﬁcient features extracted from case proﬁles (e.g., dates, locations, terms, and types).”
LexGLUE: A Benchmark Dataset for Legal Language Understanding in English (2021, background): “…2018; Aletras et al., 2019, 2020; Zhong et al., 2020b; Bommarito et al., 2021), from judgment prediction (Aletras et al., 2016; Sim et al., 2016; Katz et al., 2017; Zhong et al., 2018; Chalkidis et al., 2019a; Malik et al., 2021), information extraction from legal documents (Chalkidis et al.,…”
Legal Judgment Prediction via Topological Learning (2018, background): “Katz et al. (2017) predict the US Supreme Court’s decisions based on efﬁcient features from case proﬁles.”