Hyunseung Kang: Transfer Learning Between U.S. Presidential Elections

Speaker: Hyunseung Kang (University of Wisconsin-Madison)
Discussant: Melody Huang (Harvard University)
Title: Transfer Learning Between U.S. Presidential Elections: How much can we learn from a 2020 ad campaign to inform 2024 elections?
Abstract: In the 2020 U.S presidential election, Aggarwal et al. (2023) ran a large-scale, randomized experiment to analyze the impact of an online ad campaign on voter turnout and found that the overall impact was “effectively equivalent to zero." As the 2024 election approaches, a natural question to ask is whether a similar ad campaign would remain ineffective during this election. Despite some similarities between 2020 and 2024, such as the same presumptive candidates and concerns about the economy, differences like COVID-19, concerns about immigration, and overturning of Roe v. Wade exist, which raises the broader question: how much can we learn from past ad campaigns to inform future ad campaigns?In this ongoing work, we lay out a transfer learning framework to address this question. Two major features of our framework are that we do not assume (a) the transportability assumption, which roughly states that the differences between the 2020 and the 2024 elections can be adjusted by a common set of covariates, and (b) the same set of covariates are measured between the two elections. Instead, we present a sensitivity analysis framework that provide a plausible range of effects of future ad campaigns based on past ad campaigns and allow the covariates to be different between elections. Under our framework, we develop two nonparametric estimators, one of which is rooted in the study design, derive a bootstrap approach to conduct inference, and establish some inferential guarantees. We also present simple ways to calibrate and ultimately, demystify sensitivity parameters for interpretability. We conclude with some preliminary results about the plausible range of effects of running an ad campaign during the 2024 U.S. presidential election.This is joint work with Xinran Miao (UW-Madison) and Jiwei Zhao (UW-Madison).