Two decades of U.S. government legislative outcomes, as well as the policy preferences of rich people, the general population, and diverse interest groups, were captured in a detailed dataset curated and analyzed by Gilens, Page et al. (2014). They found that the preferences of the rich correlated strongly with policy outcomes, while the preferences of the general population did not, except via a linkage with rich people's preferences. Their analysis applied the tools of classical statistical inference, in particular logistic regression. In this paper we analyze the Gilens dataset using the complementary tools of Random Forest classifiers (RFs), from Machine Learning. We present two primary findings, concerning respectively prediction and inference: (i) Holdout test sets can be predicted with approximately 70% balanced accuracy by models that consult only the preferences of rich people and a small number of powerful interest groups, as well as policy area labels. These results include retrodiction, where models trained on pre-1997 cases predicted "future" (post-1997) cases. The 20% gain in accuracy over baseline (chance), in this detailed but noisy dataset, indicates the high importance of a few wealthy players in U.S. policy outcomes, and aligns with a body of research indicating that the U.S. government has significant plutocratic tendencies. (ii) The feature selection methods of RF models identify especially salient subsets of interest groups (economic players). These can be used to further investigate the dynamics of governmental policy making, and also offer an example of the potential value of RF feature selection methods for inference on datasets such as this.

翻译：Gilens、Page等人（2014）整理并分析了一个详细的数据集，其中包含二十年来美国政府的立法结果，以及富人、普通民众和各类利益集团的政策偏好。他们发现，富人的偏好与政策结果高度相关，而普通民众的偏好则不然，除非其与富人的偏好存在关联。他们的分析采用了经典统计推断工具，特别是逻辑回归。本文利用机器学习中的互补工具——随机森林分类器（RFs）对Gilens数据集进行分析。我们提出了两个主要发现，分别涉及预测和推断：（i）仅参考富人偏好、少数强势利益集团偏好以及政策领域标签的模型，在留出测试集上能达到约70%的平衡准确率。这些结果包括回溯预测，即使用1997年前案例训练的模型成功预测了“未来”（1997年后）的案例。在这个详细但噪声较多的数据集中，准确率较基线（随机猜测）提升20%，表明少数富裕参与者对美国政策结果具有高度重要性，并与一系列研究表明美国政府存在显著财阀化倾向的结论一致。（ii）随机森林模型的特征选择方法识别出尤为突出的利益集团（经济参与者）子集。这些子集可用于进一步探究政府政策制定的动态机制，同时也为随机森林特征选择方法在此类数据集上的推断潜力提供了示例。