George Gui, Harikesh Nair, Fengshi Niu
arXiv 30 Dec 2021 · Econometrics · publishedMarketing Science (2024) · 9 citations (OpenAlex)
arXiv:2112.15155 · PDF · DOI · OpenAlex · Extracted main text
Causally identifying the effect of digital advertising is challenging, because experimentation is expensive, and observational data lacks random variation. This paper identifies a pervasive source of naturally occurring, quasi-experimental variation in user-level ad-exposure in digital advertising campaigns. It shows how this variation can be utilized by ad-publishers to identify the causal effect of advertising campaigns. The variation pertains to auction throttling, a probabilistic method of budget pacing that is widely used to spread an ad-campaign`s budget over its deployed duration, so that the campaign`s budget is not exceeded or overly concentrated in any one period. The throttling mechanism is implemented by computing a participation probability based on the campaign`s budget spending rate and then including the campaign in a random subset of available ad-auctions each period according to this probability. We show that access to logged-participation probabilities enables identifying the local average treatment effect (LATE) in the ad-campaign. We present a new estimator that leverages this identification strategy and outline a bootstrap procedure for quantifying its variability. We apply our method to real-world ad-campaign data from an e-commerce advertising platform, which uses such throttling for budget pacing. We show our estimate is statistically different from estimates derived using other standard observational methods such as OLS and two-stage least squares estimators. Our estimated conversion lift is 110%, a more plausible number than 600%, the conversion lifts estimated using naive observational methods.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Abadie, A., Athey, S., Imbens, G. W., and Wooldridge, J. M (2020) Sampling-based versus design-based uncertainty in regression analysis | 0.874 | 5 | 2 | 100% |
| 2 | Blake, T., Nosko, C., and Tadelis, S (2015) Consumer Heterogeneity and Paid Search Effectiveness: A Large-Scale Field Experiment | 0.843 | 3 | 3 | 100% |
| 3 | Gordon, B. R., Zettelmeyer, F., Bhargava, N., and Chapsky, D (2019) A Comparison of Approaches to Advertising Measurement: Evidence from Big Field Experiments at Facebook | 0.737 | 3 | 2 | 100% |
| 4 | Hong, H., Leung, M. P., and Li, J (2020) Inference on finite-population treatment effects under limited overlap | 0.644 | 2 | 2 | 100% |
| 5 | Karande, C., Mehta, A., and Srikant, R (2013) Optimizing budget constrained spend in search advertising | 0.644 | 2 | 2 | 100% |
| 6 | Frölich, M (2007) Nonparametric iv estimation of local average treatment effects with covariates | 0.585 | 3 | 1 | 100% |
| 7 | Balseiro, S., Kim, A., Mahdian, M., and Mirrokni, V (2021) Budget-management strategies in repeated auctions | 0.511 | 2 | 1 | 100% |
| 8 | Gordon, B. R., Moakler, R., and Zettelmeyer, F (2022) Close Enough? A Large-Scale Exploration of Non-Experimental Approaches to Advertising Measurement | 0.511 | 2 | 1 | 100% |
| 9 | Abadie, A (2003) Semiparametric instrumental variable estimation of treatment response models | 0.405 | 1 | 1 | 100% |
| 10 | Angrist, J. D., Imbens, G. W., and Rubin, D. B (1996) Identification of causal effects using instrumental variables | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 36 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Close Enough? A Large-Scale Exploration of Non-Experimental Approaches to Advertising Measurement | 0.644 | 2 | 2 |
| 2 | Multi-cell experiments for marginal treatment effect estimation of digital ads | 0.405 | 1 | 1 |
| 3 | Predicted Incrementality by Experimentation (PIE) for Ad Measurement | 0.405 | 1 | 1 |