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.
Related papers
- Large Language Models (LLMs) as Agents for Augmented Democracy [6.491009626125319]
We explore the capabilities of an augmented democracy system built on off-the-shelf LLMs fine-tuned on data summarizing individual preferences.
We use a train-test cross-validation setup to estimate the accuracy with which the LLMs predict both: a subject's individual political choices and the aggregate preferences of the full sample of participants.
arXiv Detail & Related papers (2024-05-06T13:23:57Z) - Reduced-Rank Multi-objective Policy Learning and Optimization [57.978477569678844]
In practice, causal researchers do not have a single outcome in mind a priori.
In government-assisted social benefit programs, policymakers collect many outcomes to understand the multidimensional nature of poverty.
We present a data-driven dimensionality-reduction methodology for multiple outcomes in the context of optimal policy learning.
arXiv Detail & Related papers (2024-04-29T08:16:30Z) - Predicting Survey Response with Quotation-based Modeling: A Case Study
on Favorability towards the United States [0.0]
We propose a pioneering approach for predicting survey responses by examining quotations using machine learning.
We leverage a vast corpus of quotations from individuals across different nationalities to extract their level of favorability.
We employ a combination of natural language processing techniques and machine learning algorithms to construct a predictive model for survey responses.
arXiv Detail & Related papers (2023-05-23T14:11:01Z) - ASPEST: Bridging the Gap Between Active Learning and Selective
Prediction [56.001808843574395]
Selective prediction aims to learn a reliable model that abstains from making predictions when uncertain.
Active learning aims to lower the overall labeling effort, and hence human dependence, by querying the most informative examples.
In this work, we introduce a new learning paradigm, active selective prediction, which aims to query more informative samples from the shifted target domain.
arXiv Detail & Related papers (2023-04-07T23:51:07Z) - Selecting the suitable resampling strategy for imbalanced data
classification regarding dataset properties [62.997667081978825]
In many application domains such as medicine, information retrieval, cybersecurity, social media, etc., datasets used for inducing classification models often have an unequal distribution of the instances of each class.
This situation, known as imbalanced data classification, causes low predictive performance for the minority class examples.
Oversampling and undersampling techniques are well-known strategies to deal with this problem by balancing the number of examples of each class.
arXiv Detail & Related papers (2021-12-15T18:56:39Z) - Identification of Subgroups With Similar Benefits in Off-Policy Policy
Evaluation [60.71312668265873]
We develop a method to balance the need for personalization with confident predictions.
We show that our method can be used to form accurate predictions of heterogeneous treatment effects.
arXiv Detail & Related papers (2021-11-28T23:19:12Z) - Statistical discrimination in learning agents [64.78141757063142]
Statistical discrimination emerges in agent policies as a function of both the bias in the training population and of agent architecture.
We show that less discrimination emerges with agents that use recurrent neural networks, and when their training environment has less bias.
arXiv Detail & Related papers (2021-10-21T18:28:57Z) - On Modeling Human Perceptions of Allocation Policies with Uncertain
Outcomes [6.729250803621849]
We show that probability weighting can be used to make predictions about preferences over probabilistic distributions of harm and benefit.
We identify optimal policies for minimizing perceived total harm and maximizing perceived total benefit that take the distorting effects of probability weighting into account.
arXiv Detail & Related papers (2021-03-10T02:22:08Z) - Inflating Topic Relevance with Ideology: A Case Study of Political
Ideology Bias in Social Topic Detection Models [16.279854003220418]
We investigate the impact of political ideology biases in training data.
Our work highlights the susceptibility of large, complex models to propagating the biases from human-selected input.
As a way to mitigate the bias, we propose to learn a text representation that is invariant to political ideology while still judging topic relevance.
arXiv Detail & Related papers (2020-11-29T05:54:03Z) - Magnify Your Population: Statistical Downscaling to Augment the Spatial
Resolution of Socioeconomic Census Data [48.7576911714538]
We present a new statistical downscaling approach to derive fine-scale estimates of key socioeconomic attributes.
For each selected socioeconomic variable, a Random Forest model is trained on the source Census units and then used to generate fine-scale gridded predictions.
As a case study, we apply this method to Census data in the United States, downscaling the selected socioeconomic variables available at the block group level, to a grid of 300 spatial resolution.
arXiv Detail & Related papers (2020-06-23T16:52:18Z) - Interventions for Ranking in the Presence of Implicit Bias [34.23230188778088]
Implicit bias is the unconscious attribution of particular qualities (or lack thereof) to a member from a particular social group.
Rooney Rule is a constraint to improve the utility of the outcome for certain cases of the subset selection problem.
We present a family of simple and interpretable constraints and show that they can optimally mitigate implicit bias.
arXiv Detail & Related papers (2020-01-23T19:11:31Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.