2015 WhoSupportedObamain2012Ecologic

• (Flaxman et al., 2015) ⇒ Seth R. Flaxman, Yu-Xiang Wang, and Alexander J. Smola. (2015). “Who Supported Obama in 2012?: Ecological Inference through Distribution Regression.” In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2015). ISBN:978-1-4503-3664-2 doi:10.1145/2783258.2783300

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222015%22+Who+Supported+Obama+in+2012%3F%3A+Ecological+Inference+through+Distribution+Regression
• http://dl.acm.org/citation.cfm?id=2783258.2783300&preflayout=flat#citedby

Quotes

Author Keywords

• Correlation and regression analysis; distribution regression; gaussian processes; kernel methods; machine learning; multivariate statistics; nonparametric statistics; supervised learning

Abstract

We present a new solution to the ecological inference problem, of learning individual-level associations from aggregate data. This problem has a long history and has attracted much attention, debate, claims that it is unsolvable, and purported solutions. Unlike other ecological inference techniques, our method makes use of unlabeled individual-level data by embedding the distribution over these predictors into a vector in Hilbert space. Our approach relies on recent learning theory results for distribution regression, using kernel embeddings of distributions. Our novel approach to distribution regression exploits the connection between Gaussian process regression and kernel ridge regression, giving us a coherent, Bayesian approach to learning and inference and a convenient way to include prior information in the form of a spatial covariance function. Our approach is highly scalable as it relies on FastFood, a randomized explicit feature representation for kernel embeddings. We apply our approach to the challenging political science problem of modeling the voting behavior of demographic groups based on aggregate voting data. We consider the 2012 US Presidential election, and ask: what was the probability that members of various demographic groups supported Barack Obama, and how did this vary spatially across the country? Our results match standard survey-based exit polling data for the small number of states for which it is available, and serve to fill in the large gaps in this data, at a much higher degree of granularity.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2015 WhoSupportedObamain2012Ecologic	Alexander J. Smola Seth R. Flaxman Yu-Xiang Wang			Who Supported Obama in 2012?: Ecological Inference through Distribution Regression				10.1145/2783258.2783300		2015