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A General Approach For Predicting The Behavior Of The Supreme Court Of The United States, Daniel Katz Apr 2017

A General Approach For Predicting The Behavior Of The Supreme Court Of The United States, Daniel Katz

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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. 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 ...