ACC
Causal Inference-based Identification of Incremental Effects in Digital Advertising and Optimization Pathways for Resource Allocation
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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.
Keywords
Causal inference; digital advertising; incremental effect; random retention experiment
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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
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