Applied Statistics Workshop by John Cherian

John Cherian

(Stanford University)

Election Modeling in 2024: A Conformal Inference Approach

Abstract :
We consider a high-stakes application of statistical inference: uncertainty quantification for election night modeling. In this problem, the analyst observes vote counts from early-reporting jurisdictions, e.g., precincts on the East Coast of the United States, and fits a model to these results that predicts the final outcome in each contested race. Quantifying the error of this prediction is crucial; an overconfident prediction can mislead the public and harm the news provider’s reputation. Over the last four years, we have worked on methods to extend conformal prediction, a popular method for assumption-lean inference, to this setting. Working with election data poses many challenges. For example, the data-generating distribution shifts over time, and spatiotemporal correlation can invalidate standard approaches. Variants of our model have been featured in The Washington Post’s coverage of the 2020 and 2022 national United States elections, and the model introduced in this talk will be used in this fall’s presidential election. This is joint work with Lenny Bronner (The Washington Post) and Emmanuel Candes (Stanford).

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