IDEAS home Printed from https://ideas.repec.org/a/inm/ormksc/v43y2024i4p903-917.html
   My bibliography  Save this article

Latent Stratification for Incrementality Experiments

Author

Listed:
  • Ron Berman

    (The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104)

  • Elea McDonnell Feit

    (LeBow College of Business, Drexel University, Philadelphia, Pennsylvania 19104)

Abstract

Incrementality experiments compare customers exposed to a marketing action designed to increase sales with those randomly assigned to a control group. These experiments suffer from noisy responses, which make precise estimation of the average treatment effect (ATE) and marketing return difficult. We develop a model that improves the precision by estimating separate treatment effects for three latent strata defined by potential outcomes in the experiment—customers who would buy regardless of ad exposure, those who would buy only if exposed to ads, and those who would not buy regardless. The overall ATE is estimated by averaging the strata-level effects, and this produces a more precise estimator of the ATE over a wide range of conditions typical of marketing experiments. Analytical results and simulations show that the method decreases the sampling variance of the ATE most when (1) there are large differences in the treatment effect between latent strata and (2) the model used to estimate the strata-level effects is well identified. Applying the procedure to five catalog experiments shows a reduction of 30%–60% in the variance of the overall ATE. This leads to a substantial decrease in decision errors when the estimator is used to determine whether ads should be continued or discontinued.

Suggested Citation

  • Ron Berman & Elea McDonnell Feit, 2024. "Latent Stratification for Incrementality Experiments," Marketing Science, INFORMS, vol. 43(4), pages 903-917, July.
    • Handle: RePEc:inm:ormksc:v:43:y:2024:i:4:p:903-917
    DOI: 10.1287/mksc.2022.0297
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mksc.2022.0297
    Download Restriction: no
    File URL: https://libkey.io/10.1287/mksc.2022.0297?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Duncan Simester & Artem Timoshenko & Spyros I. Zoumpoulis, 2020. "Efficiently Evaluating Targeting Policies: Improving on Champion vs. Challenger Experiments," Management Science, INFORMS, vol. 66(8), pages 3412-3424, August.
    2. Constantine E. Frangakis & Donald B. Rubin, 2002. "Principal Stratification in Causal Inference," Biometrics, The International Biometric Society, vol. 58(1), pages 21-29, March.
    3. Susan Athey & Raj Chetty & Guido W. Imbens & Hyunseung Kang, 2019. "The Surrogate Index: Combining Short-Term Proxies to Estimate Long-Term Treatment Effects More Rapidly and Precisely," NBER Working Papers 26463, National Bureau of Economic Research, Inc.
    4. Brett R. Gordon & Florian Zettelmeyer & Neha Bhargava & Dan Chapsky, 2019. "A Comparison of Approaches to Advertising Measurement: Evidence from Big Field Experiments at Facebook," Marketing Science, INFORMS, vol. 38(2), pages 193-225, March.
    5. Presnell, Brett & Boos, Dennis D., 2004. "The IOS Test for Model Misspecification," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 216-227, January.
    6. 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.
    7. Luke W. Miratrix & Jasjeet S. Sekhon & Bin Yu, 2013. "Adjusting treatment effect estimates by post-stratification in randomized experiments," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(2), pages 369-396, March.
    8. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, November.
    9. Randall A. Lewis & Justin M. Rao, 2015. "The Unfavorable Economics of Measuring the Returns to Advertising," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 130(4), pages 1941-1973.
    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. Brett R. Gordon & Robert Moakler & Florian Zettelmeyer, 2023. "Predictive Incrementality by Experimentation (PIE) for Ad Measurement," Papers 2304.06828, arXiv.org.
    2. Günter J. Hitsch & Sanjog Misra & Walter W. Zhang, 2024. "Heterogeneous treatment effects and optimal targeting policy evaluation," Quantitative Marketing and Economics (QME), Springer, vol. 22(2), pages 115-168, June.
    3. 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.
    4. Susan Athey & Kristen Grabarz & Michael Luca & Nils Wernerfelt, 2023. "Digital public health interventions at scale: The impact of social media advertising on beliefs and outcomes related to COVID vaccines," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 120(5), pages 2208110120-, January.
    5. 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.
    6. Tatsushi Oka & Shota Yasui & Yuta Hayakawa & Undral Byambadalai, 2024. "Regression Adjustment for Estimating Distributional Treatment Effects in Randomized Controlled Trials," Papers 2407.14074, arXiv.org, revised Jan 2025.
    7. Carlos Fernández-Loría & Foster Provost, 2022. "Causal Decision Making and Causal Effect Estimation Are Not the Same…and Why It Matters," INFORMS Joural on Data Science, INFORMS, vol. 1(1), pages 4-16, April.
    8. Susan Athey & Kristen Grabarz & Michael Luca & Nils Wernerfelt, 2022. "The Effectiveness of Digital Interventions on COVID-19 Attitudes and Beliefs," Papers 2206.10214, arXiv.org.
    9. Zhexiao Lin & Pablo Crespo, 2024. "Variance reduction combining pre-experiment and in-experiment data," Papers 2410.09027, arXiv.org.
    10. 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.
    11. 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.
    12. 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.
    13. Davide Viviano & Jelena Bradic, 2019. "Synthetic learner: model-free inference on treatments over time," Papers 1904.01490, arXiv.org, revised Aug 2022.
    14. Rina Friedberg & Julie Tibshirani & Susan Athey & Stefan Wager, 2018. "Local Linear Forests," Papers 1807.11408, arXiv.org, revised Sep 2020.
    15. Rachel Axelrod & Daniel Nevo, 2023. "A sensitivity analysis approach for the causal hazard ratio in randomized and observational studies," Biometrics, The International Biometric Society, vol. 79(3), pages 2743-2756, September.
    16. Koch, Bernard & Sainburg, Tim & Geraldo, Pablo & JIANG, SONG & Sun, Yizhou & Foster, Jacob G., 2021. "Deep Learning of Potential Outcomes," SocArXiv aeszf, Center for Open Science.
    17. Undral Byambadalai & Tatsushi Oka & Shota Yasui, 2024. "Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction," Papers 2407.16037, arXiv.org.
    18. Ta-Wei Huang & Eva Ascarza, 2024. "Doing More with Less: Overcoming Ineffective Long-Term Targeting Using Short-Term Signals," Marketing Science, INFORMS, vol. 43(4), pages 863-884, July.
    19. Plamen Nikolov & Hongjian Wang & Kevin Acker, 2020. "Wage premium of Communist Party membership: Evidence from China," Pacific Economic Review, Wiley Blackwell, vol. 25(3), pages 309-338, August.
    20. Yiyi Huo & Yingying Fan & Fang Han, 2023. "On the adaptation of causal forests to manifold data," Papers 2311.16486, arXiv.org, revised Dec 2023.

    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:inm:ormksc:v:43:y:2024:i:4:p:903-917. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.