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Close Enough? A Large-Scale Exploration of Non-Experimental Approaches to Advertising Measurement

Citations

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Cited by:

  1. Helal, Al Mansor & Hiraki, Ryotaro & Patrinos, Harry Anthony, 2026. "Returns to Education in the United States: A Comparison of OLS and Double Machine Learning Methods," GLO Discussion Paper Series 1733, Global Labor Organization (GLO).
  2. Tsung-Yiou Hsieh & Rex Yuxing Du & Shijie Lu, 2026. "Leveraging Large-Scale Granular Single-Source Data for TV Advertising: An Identification Strategy," Marketing Science, INFORMS, vol. 45(3), pages 632-652, May.
  3. Ryan Dew & Nicolas Padilla & Anya Shchetkina, 2024. "Your MMM is Broken: Identification of Nonlinear and Time-varying Effects in Marketing Mix Models," Papers 2408.07678, arXiv.org.
  4. Ali Goli & Jason Huang & David Reiley & Nickolai M. Riabov, 2025. "Measuring consumer sensitivity to audio advertising: a long-run field experiment on Pandora internet radio," Quantitative Marketing and Economics (QME), Springer, vol. 23(3), pages 347-377, September.
  5. Daniel Guhl & Friederike Paetz & Udo Wagner & Michel Wedel, 2024. "Predicting and optimizing marketing performance in dynamic markets," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 46(1), pages 1-27, March.
  6. Shota Yasui & Tatsushi Oka & Undral Byambadalai & Yuki Oishi, 2026. "Distributional treatment effects of content promotion: evidence from an ABEMA field experiment," The Japanese Economic Review, Springer, vol. 77(2), pages 391-406, April.
  7. Henrika Langen & Martin Huber, 2023. "How causal machine learning can leverage marketing strategies: Assessing and improving the performance of a coupon campaign," PLOS ONE, Public Library of Science, vol. 18(1), pages 1-37, January.
  8. Ali Goli & Jason Huang & David Reiley & Nickolai M. Riabov, 2024. "Measuring Consumer Sensitivity to Audio Advertising: A Long-Run Field Experiment on Pandora Internet Radio," Papers 2412.05516, arXiv.org.
  9. Berman, Ron & Heller, Yuval, 2025. "Naive analytics: The strategic advantage of algorithmic heuristics," Games and Economic Behavior, Elsevier, vol. 154(C), pages 62-78.
  10. Paul B. Ellickson & Wreetabrata Kar & James C. Reeder, 2023. "Estimating Marketing Component Effects: Double Machine Learning from Targeted Digital Promotions," Marketing Science, INFORMS, vol. 42(4), pages 704-728, July.
  11. Francis J. DiTraglia & Laura Liu, 2025. "Bayesian Double Machine Learning for Causal Inference," Papers 2508.12688, arXiv.org.
  12. Gordon Burtch & Robert Moakler & Brett R. Gordon & Poppy Zhang & Shawndra Hill, 2025. "Characterizing and Minimizing Divergent Delivery in Meta Advertising Experiments," Papers 2508.21251, arXiv.org.
  13. Weijia Dai & Hyunjin Kim & Michael Luca, 2023. "Frontiers: Which Firms Gain from Digital Advertising? Evidence from a Field Experiment," Marketing Science, INFORMS, vol. 42(3), pages 429-439, May.
  14. Caio Waisman & Brett R. Gordon, 2023. "Multicell experiments for marginal treatment effect estimation of digital ads," Papers 2302.13857, arXiv.org, revised Apr 2025.
  15. Moritz von Zahn & Arda Güler & Jella Pfeiffer & Hajo A. Reijers & Oliver Hinz, 2026. "Causal Machine Learning in Information Systems Research," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 68(2), pages 235-242, April.
  16. Boegershausen, Johannes & Cornil, Yann & Yi, Shangwen & Hardisty, David J., 2025. "On the persistent mischaracterization of Google and Facebook A/B tests: How to conduct and report online platform studies," International Journal of Research in Marketing, Elsevier, vol. 42(3), pages 886-903.
  17. Jonathan Fuhr & Philipp Berens & Dominik Papies, 2024. "Estimating Causal Effects with Double Machine Learning -- A Method Evaluation," Papers 2403.14385, arXiv.org, revised Apr 2024.
  18. Guy Aridor & Rafael Jiménez-Durán & Ro'ee Levy & Lena Song, 2024. "The Economics of Social Media," Journal of Economic Literature, American Economic Association, vol. 62(4), pages 1422-1474, December.
  19. Jahani, Eaman & Kolic, Blas & Tonneau, Manuel & Lin, Hause & Barkoczi, Daniel & Fraiberger, Samuel P., 2026. "Celebrity messages reduce online hate and limit its spread," SocArXiv qmvuh_v1, Center for Open Science.
  20. Julian Runge & Koen Pauwels, 2026. "Open-Source Media and Marketing Mix Modeling: Practice-Oriented Overview, Challenges, Opportunities," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 13(1), pages 1-16, December.
  21. Herhausen, Dennis & Bernritter, Stefan F. & Ngai, Eric W.T. & Kumar, Ajay & Delen, Dursun, 2024. "Machine learning in marketing: Recent progress and future research directions," Journal of Business Research, Elsevier, vol. 170(C).
  22. Harang Ju & Michael Zhao & Sinan Aral, 2025. "Advertising Spillovers in Mobile Apps: Evidence from Ad Shutoffs and Store Rankings," Papers 2504.16151, arXiv.org, revised Jul 2026.
  23. Duhirwe, Patrick Nzivugira & Ngarambe, Jack & Yun, Geun Young, 2024. "Causal effects of policy and occupant behavior on cooling energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 206(C).
  24. Brett R. Gordon & Robert Moakler & Florian Zettelmeyer, 2023. "Predicted Incrementality by Experimentation (PIE) for Ad Measurement," Papers 2304.06828, arXiv.org, revised Apr 2026.
  25. Guy Aridor & Rafael Jiménez-Durán & Ro'ee Levy & Lena Song, 2024. "Experiments on Social Media," CESifo Working Paper Series 11275, CESifo.
  26. Jonathan Fuhr & Dominik Papies, 2024. "Double Machine Learning meets Panel Data -- Promises, Pitfalls, and Potential Solutions," Papers 2409.01266, arXiv.org.
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