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A General Approach For Predicting The Behavior Of The Supreme Court Of The United States, Daniel Katz
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, we achieve …
Class Actions And The Counterrevolution Against Federal Litigation, Stephen B. Burbank, Sean Farhang
Class Actions And The Counterrevolution Against Federal Litigation, Stephen B. Burbank, Sean Farhang
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In this article we situate consideration of class actions in a framework, and fortify it with data, that we have developed as part of a larger project, the goal of which is to assess the counterrevolution against private enforcement of federal law from an institutional perspective. In a series of articles emerging from the project, we have documented how the Executive, Congress and the Supreme Court (wielding both judicial power under Article III of the Constitution and delegated legislative power under the Rules Enabling Act) fared in efforts to reverse or dull the effects of statutory and other incentives for …