Transfer Learning Between U.S. Presidential Elections: How Should We Learn From A 2020 Ad Campaign To Inform 2024 Ad Campaigns?

TL;DR

This paper introduces a transfer learning framework to estimate the impact of digital political ads on voter turnout in 2024, leveraging past election data to reduce costs and improve efficiency.

Contribution

It develops a novel transfer learning approach with sensitivity analysis for estimating ad effects across elections, offering practical estimators for political campaign analysis.

Findings

Effect heterogeneity across counties and subgroups

Negative ads may influence voter turnout variably

Framework enables cost-effective election impact evaluation

Abstract

For the 2024 U.S. presidential election, would negative, digital ads against Donald Trump impact voter turnout in Pennsylvania (PA), a key "tipping point'' state? The gold standard to address this question, a randomized experiment where voters get randomized to different ads, yields unbiased estimates of the ad effect, but is very expensive. Instead, we propose a less-than-ideal, but significantly cheaper and faster framework based on transfer learning, where we transfer knowledge from a past ad experiment in 2020 to evaluate ads for 2024. A key component of our framework is a sensitivity analysis that quantifies the unobservable differences between 2020 and 2024 elections, where sensitivity parameters can be calibrated in a data-driven manner. We propose two estimators of the 2024 ad effect: a simple regression estimator with bootstrap, which we recommend for practitioners in this field, and an estimator based on the efficient influence function for broader applications. Using our framework, we estimate the effect of running a negative, digital ad campaign against Trump on voter turnout in PA for the 2024 election. Our findings indicate effect heterogeneity across counties of PA and among important subgroups stratified by gender, urbanicity, and education attainment.