Predicting United States policy outcomes with Random Forests
- URL: http://arxiv.org/abs/2008.07338v1
- Date: Sun, 2 Aug 2020 18:06:57 GMT
- Title: Predicting United States policy outcomes with Random Forests
- Authors: Shawn McGuire, Charles Delahunt
- Abstract summary: 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.
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.
We present two primary findings, concerning respectively prediction and inference.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: 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.
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