Predicting Congressional Votes Based on Campaign Finance Data

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Authors: Samuel Smith, Jae Yeon (Claire) Baek, Zhaoyi Kang, Dawn Song, Laurent El Ghaoui, Mario Frank.

Status: In Proc. International Conference on Machine Learning and Applications (ICMLA), December 2012.

Abstract: The USA is witnessing a heavy debate about the influence of political campaign contributions and votes cast on the floor of the United States Congress. We contribute quantitative arguments to this predominantly qualitative discussion by analyzing a dataset of political campaign contributions. We validate that the campaign donations of politicians are mainly influenced by his or her political power and affiliation to a political party. Approaching the question of whether donations influence votes, we employ supervised learning techniques to classify how a politician will vote based solely upon from whom he or she received donations. The statistical significance of the results are assessed within the context of the debate currently surrounding campaign finance reform. Our experimental findings exhibit a large predictive power of the donations, demonstrating high informativeness of the donations with respect to voting outcomes. However, observing the slightly superior accuracy of the party line as a predictor, a causal relationship between donations and votes cannot be identified.

Bibtex reference:

@conference{Smi:12,
  Author = {Samuel Smith and Jae Yeon (Claire) Baek and Zhaoyi Kang and Dawn Song and Laurent {El Ghaoui} and Mario Frank},
  Title = {Predicting Congressional Votes Based on Campaign Finance Data},
  Booktitle= {Proc. International Conference on Machine Learning and Applications ({ICMLA})},
  Year = {2012},
  Month = dec
}