Imai, Kosuke, and Aaron Strauss. (2011). ``Estimation of Heterogeneous Treatment Effects from Randomized Experiments, with Application to the Optimal Planning of the Get-out-the-vote Campaign.'' Political Analysis, Vol. 19, No. 1 (Winter), pp. 1-19. Winner of Political Analysis Editors' Choice Award.

Abstract

Political scientists have recently conducted hundreds of randomized field experiments to examine the effectiveness of various mobilization methods for increasing voter turnout. Given the high degree of internal and external validity, the empirical findings of these studies have a potential to significantly impact the practice of get-out-the-vote (GOTV) campaigns in the real world. In this paper, we offer an essential and yet missing methodological tool that allows GOTV campaign planners to best utilize the results of such field experiments. In particular, we show how to derive the optimal GOTV campaign strategy from field experiments. Our nonparametric method is applicable to partisan or nonpartisan campaigns as well as campaigns with multiple mobilization methods of the same or different costs. We evaluate the effectiveness of the proposed method using three existing field experiments. In multiple cases, we find that the resulting optimal campaign strategy is more than twice as cost-effective as a naive strategy.

Related Papers

Imai, Kosuke and Michael Lingzhi Li (2025). ``Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments.'' Journal of Business & Economic Statistics, Vol. 43, No. 1, pp. 256-268.
Jia, Zeyang, Kosuke Imai, and Michael Lingzhi Li (2025). ``Cramming Contextual Bandits for On-policy Statistical Evaluation.''
Li, Michael Lingzhi and Kosuke Imai (2024). ``Neyman Meets Causal Machine Learning: Experimental Evaluation of Individualized Treatment Rules.'' Journal of Causal Inference, Vol 12, No. 1, pp. 1-20. Special Issue on Neyman (1923) and its influences on causal inference
Imai, Kosuke and Michael Lingzhi Li (2023). ``Experimental Evaluation of Individualized Treatment Rules.'' Journal of the American Statistical Association, Vol. 118, No. 541, pp. 242-256.
© Kosuke Imai