Caio Waisman, Harikesh S. Nair, Carlos Carrion
arXiv 22 Aug 2019 · Machine Learning · publishedMarketing Science (2024) · 9 citations (OpenAlex)
arXiv:1908.08600 · PDF · DOI · OpenAlex · Extracted main text
Real-time bidding (RTB) systems, which utilize auctions to allocate user impressions to competing advertisers, continue to enjoy success in digital advertising. Assessing the effectiveness of such advertising remains a challenge in research and practice. This paper proposes a new approach to perform causal inference on advertising bought through such mechanisms. Leveraging the economic structure of first- and second-price auctions, we first show that the effects of advertising are identified by the optimal bids. Hence, since these optimal bids are the only objects that need to be recovered, we introduce an adapted Thompson sampling (TS) algorithm to solve a multi-armed bandit problem that succeeds in recovering such bids and, consequently, the effects of advertising while minimizing the costs of experimentation. We derive a regret bound for our algorithm which is order optimal and use data from RTB auctions to show that it outperforms commonly used methods that estimate the effects of advertising.
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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 | Bompaire, M., Gilotte, A., and Heymann, B (2021) Causal models for real time bidding with repeated user interactions | 0.830 | 7 | 2 | 86% |
| 2 | Tunuguntla, S. and Hoban, P. R (2021) A near-optimal bidding strategy for real-time display advertising auctions | 0.737 | 4 | 2 | 75% |
| 3 | Moriwaki, D., Hayakawa, Y., Munemasa, I., Saito, Y., and Matsui, A (2020) Unbiased lift-based bidding system | 0.693 | 5 | 1 | 100% |
| 4 | Nie, X., Tian, X., Taylor, J., and Zou, J (2018) Why adaptively collected data have negative bias and how to correct for it | 0.585 | 3 | 1 | 100% |
| 5 | Waisman, C., Nair, H. S., Carrion, C., and Xu, N (2019) Online inference for advertising auctions self | 0.585 | 3 | 1 | 100% |
| 6 | Xu, J., Shao, X., Ma, J., Lee, Kuang-chih, Q. H., and Lu, Q (2016) Lift-based bidding in ad selection | 0.585 | 3 | 1 | 100% |
| 7 | Albert, J. H. and Chib, S (1993) Bayesian analysis of binary and polychotomous response data | 0.511 | 2 | 2 | 50% |
| 8 | Cai, H., Ren, K., Zhang, W., Malialis, K., Wang, J., Yu, Y., and Guo… (2017) Real-time bidding by reinforcement learning in display advertising | 0.511 | 2 | 2 | 50% |
| 9 | Ju, N., Hu, D., Henderson, A., and Hong, L (2019) A sequential test for selecting the better variant: Online A/B testing, adaptive allocation, and continuous monitoring | 0.511 | 2 | 1 | 100% |
| 10 | Kallus, N (2018) Instrument-armed bandits | 0.511 | 2 | 1 | 100% |
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