ArticleOpen Access http://dx.doi.org/10.26855/acc.2026.06.006
Causal Inference-based Identification of Incremental Effects in Digital Advertising and Optimization Pathways for Resource Allocation
Yiyu Yang
School of Professional Studies, Columbia University, New York, NY 10027, USA.
*Corresponding author: Yiyu Yang
Published: June 24, 2026
Abstract
Digital advertising placement has formed a high-frequency, automated, and cross-channel data feedback system. However, exposure, clicks, and conversion records often fail to directly demonstrate the true incremental contribution of the advertisement. Causal inference can separate natural conversions, user intention differences, and channel position deviations from the advertising effect by means of counterfactual comparison, random retention experiments, geographical experiments, difference-in-differences, and observational data correction methods. This enables the identification of the net impact of advertising reach on purchases, repeat purchases, and customer value. By conducting calculations around incremental conversion rate, incremental ROAS, marginal acquisition cost, and confidence intervals, it is possible to recalibrate channel budgets, audience bids, frequency control, and creative expansion sequence, shifting the allocation of advertising resources from surface attribution to a focus on incremental contributions. Furthermore, the integration of causal identification results into routine campaign review and budget adjustment can help advertisers compare channel performance under a more stable evaluative standard, reduce repeated investment in low-incrementality audiences, and establish a more disciplined allocation logic between short-term conversion targets and long-term customer value.
Keyword
Causal inference; digital advertising; incremental effect; random retention experiment
References
[1] Li Y, Zhuang M, Fang E. Multichannel effects of mobile in-feed advertising. J Mark. 2026;90(1):51-71.
[2] Su S, Guo X, Tang H, et al. Countdown timers improve online video advertising effectiveness: evidence from eye-tracking data. Eur J Mark. 2025;59(11):2642-2671.
[3] Vo VT, Lee Y, Leong YT. Federated causal inference from observational data. Mach Learn. 2025;114(8):194.
[4] Souissi B, Elleuch AM, Ghorbel A. A novel combined fuzzy VIKOR and mathematical optimization programming for online advertising effectiveness. OPSEARCH. 2024;(prepublish):1-33.
[5] Bram D, Catalina G. The regulation of digital advertising under the DSA: a critical assessment. Comput Law Secur Rev. 2023;51.
[6] R. R. An introduction to grey influence analysis (GINA): applications to causal modelling in marketing and supply chain research. Expert Syst Appl. 2023;212.
[7] Todri V, Ghose A, Singh VP. Trade-offs in online advertising: advertising effectiveness and annoyance dynamics across the purchase funnel. Inf Syst Res. 2020;31(1):ii-v+1-295+C2.
[8] Todri V, Ghose A, Singh VP. Trade-offs in online advertising: advertising effectiveness and annoyance dynamics across the purchase funnel. Inf Syst Res. 2019.
[9] Gordon RB, Zettelmeyer F, Bhargava N, et al. A comparison of approaches to advertising measurement: evidence from big field experiments at Facebook. Mark Sci. 2019;38(2):193-225.
Copyright
© 2026 by the author(s).
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives (CC BY-NC-ND) license, which permits non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited and is not modified or adapted.
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How to cite this paper
Causal Inference-based Identification of Incremental Effects in Digital Advertising and Optimization Pathways for Resource Allocation
How to cite this paper: Yiyu Yang. (2026) Causal Inference-based Identification of Incremental Effects in Digital Advertising and Optimization Pathways for Resource Allocation. Advances in Computer and Communication, 7(2), 91-95.
DOI: http://dx.doi.org/10.26855/acc.2026.06.006