预测美国最高法院行为的一般方法
A General Approach for Predicting the Behavior of the Supreme Court of the United States
摘要：
基于机器学习的发展和司法预测科学的先前工作，我们构建了一个模型，旨在预测美国最高法院在广义的样本外环境中的行为。为此，我们开发了一个时间演化的随机森林分类器，它利用一些独特的特征工程来预测近两个世纪（1816-2015）的240，000多个司法投票和28，000个案件结果。仅使用决策前可用的数据，我们的模型在参数和非参数检验下的正义和案例水平上都优于空（基线）模型。在近两个世纪的时间里，我们在案件结果水平上实现了70.2%的准确率，在司法投票水平上实现了71.9%的准确率。最近，在过去的一个世纪里，我们的表现比样本内优化的空模型高出近5%。我们的业绩与先前工作所显示的一般预测水平一致，并有所改进;然而，我们的模型是独特的，因为它可以在样本之外适用于法院的整个过去和未来，而不是一个任期。我们的研究结果代表了定量法律预测科学的重要进展，并预示着一系列其他潜在的应用。
英文摘要：
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 which leverages some 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.
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