IDEAS home Printed from https://ideas.repec.org/p/zbw/i4rdps/237.html

A comment on "A 2 million-person, campaign-wide field experiment shows how digital advertising affects voter turnout"

Author

Listed:
  • Geissler, Dominique
  • Maarouf, Abdurahman
  • Bär, Dominik
  • Pröllochs, Nicolas
  • Feuerriegel, Stefan

Abstract

Aggarwal et al. (2023) analyze the effects of an 8-month-long advertising program on voter turnout in the 2020 US presidential election. Therein, 2 million voters were exposed to pro-Biden and anti-Trump advertisements on social media in five battleground states. The study finds no average treatment effect on voter turnout but differential effects when modeling by Trump support: Biden supporters are 0.4 percentage points more likely to vote while Trump supporters are 0.3 percentage points less likely to vote (t = −2.09 with p-value

Suggested Citation

  • Geissler, Dominique & Maarouf, Abdurahman & Bär, Dominik & Pröllochs, Nicolas & Feuerriegel, Stefan, 2025. "A comment on "A 2 million-person, campaign-wide field experiment shows how digital advertising affects voter turnout"," I4R Discussion Paper Series 237, The Institute for Replication (I4R).
  • Handle: RePEc:zbw:i4rdps:237
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/319835/1/I4R-DP237.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Stefan Wager & Susan Athey, 2018. "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1228-1242, July.
    2. Abel Brodeur & Anna Dreber & Fernando Hoces de la Guardia & Edward Miguel, 2024. "Reproduction and replication at scale," Nature Human Behaviour, Nature, vol. 8(1), pages 2-3, January.
    3. Minali Aggarwal & Jennifer Allen & Alexander Coppock & Dan Frankowski & Solomon Messing & Kelly Zhang & James Barnes & Andrew Beasley & Harry Hantman & Sylvan Zheng, 2023. "A 2 million-person, campaign-wide field experiment shows how digital advertising affects voter turnout," Nature Human Behaviour, Nature, vol. 7(3), pages 332-341, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lechner, Michael, 2018. "Modified Causal Forests for Estimating Heterogeneous Causal Effects," IZA Discussion Papers 12040, IZA Network @ LISER.
    2. William Arbour, 2021. "Can Recidivism be Prevented from Behind Bars? Evidence from a Behavioral Program," Working Papers tecipa-683, University of Toronto, Department of Economics.
    3. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-dimensional econometrics and regularized GMM," CeMMAP working papers CWP35/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Dimitris Bertsimas & Agni Orfanoudaki & Rory B. Weiner, 2020. "Personalized treatment for coronary artery disease patients: a machine learning approach," Health Care Management Science, Springer, vol. 23(4), pages 482-506, December.
    5. Jiang, Mobing & Chen, Xinyu & Xiao, Mingyue & Zhang, Yuning & Wen, Wu & Chen, Xiaohua, 2025. "From connect to conquer: capital market liberalization and Chinese firms' cross-border mergers and acquisitions," Pacific-Basin Finance Journal, Elsevier, vol. 93(C).
    6. Justin Whitehouse & Qizhao Chen & Morgane Austern & Vasilis Syrgkanis, 2025. "Inference on Optimal Policy Values and Other Irregular Functionals via Softmax Smoothing," Papers 2507.11780, arXiv.org, revised Mar 2026.
    7. Nicolaj N. Mühlbach, 2020. "Tree-based Synthetic Control Methods: Consequences of moving the US Embassy," CREATES Research Papers 2020-04, Department of Economics and Business Economics, Aarhus University.
    8. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP72/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    9. Shonosuke Sugasawa & Hisashi Noma, 2021. "Efficient screening of predictive biomarkers for individual treatment selection," Biometrics, The International Biometric Society, vol. 77(1), pages 249-257, March.
    10. Ruoxuan Xiong & Allison Koenecke & Michael Powell & Zhu Shen & Joshua T. Vogelstein & Susan Athey, 2021. "Federated Causal Inference in Heterogeneous Observational Data," Papers 2107.11732, arXiv.org, revised Apr 2023.
    11. Arne Henningsen & Guy Low & David Wuepper & Tobias Dalhaus & Hugo Storm & Dagim Belay & Stefan Hirsch, 2024. "Estimating Causal Effects with Observational Data: Guidelines for Agricultural and Applied Economists," IFRO Working Paper 2024/03, University of Copenhagen, Department of Food and Resource Economics.
    12. Bingnan Guo & Yuren Qian & Xinyan Guo & Hao Zhang, 2025. "Impact of Zero-Waste City Pilot Policies on Urban Energy Consumption Intensity: Causal Inference Based on Double Machine Learning," Sustainability, MDPI, vol. 17(11), pages 1-25, May.
    13. Hayakawa, Kazunobu & Keola, Souknilanh & Silaphet, Korrakoun & Yamanouchi, Kenta, 2022. "Estimating the impacts of international bridges on foreign firm locations: a machine learning approach," IDE Discussion Papers 847, Institute of Developing Economies, Japan External Trade Organization(JETRO).
    14. Davide Viviano & Jelena Bradic, 2019. "Synthetic learner: model-free inference on treatments over time," Papers 1904.01490, arXiv.org, revised Aug 2022.
    15. Naguib, Costanza, 2019. "Estimating the Heterogeneous Impact of the Free Movement of Persons on Relative Wage Mobility," Economics Working Paper Series 1903, University of St. Gallen, School of Economics and Political Science.
    16. Labro, Eva & Lang, Mark & Omartian, James D., 2023. "Predictive analytics and centralization of authority," Journal of Accounting and Economics, Elsevier, vol. 75(1).
    17. Rina Friedberg & Julie Tibshirani & Susan Athey & Stefan Wager, 2018. "Local Linear Forests," Papers 1807.11408, arXiv.org, revised Sep 2020.
    18. Ke-Lin Du & Rengong Zhang & Bingchun Jiang & Jie Zeng & Jiabin Lu, 2025. "Foundations and Innovations in Data Fusion and Ensemble Learning for Effective Consensus," Mathematics, MDPI, vol. 13(4), pages 1-49, February.
    19. Liangyuan Hu & Lihua Li, 2022. "Using Tree-Based Machine Learning for Health Studies: Literature Review and Case Series," IJERPH, MDPI, vol. 19(23), pages 1-13, December.
    20. Wu, Libo & Zhou, Yang, 2025. "Social norms and energy conservation in China," Resource and Energy Economics, Elsevier, vol. 82(C).

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zbw:i4rdps:237. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://www.i4replication.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.