Jointly Optimizing Debiased CTR and Uplift for Coupons Marketing: A Unified Causal Framework
arXiv:2602.12972v2 Announce Type: replace-cross Abstract: In online advertising, marketing interventions such as coupons introduce significant confounding bias into Click-Through Rate (CTR) prediction. Observed clicks reflect a mixture of users' intrinsic preferences and the uplift induced by these interventions. This causes conventional models to miscalibrate base CTRs, which distorts downstream ranking and billing decisions. Furthermore, marketing interventions often operate as multi-valued treatments with varying magnitudes, introducing additional complexity to CTR prediction. To address th
The increasing sophistication of AI models and the pervasive use of online advertising necessitate more accurate and less biased prediction systems for effective marketing ROI.
This research directly addresses a core challenge in online advertising, promising more efficient marketing spend and improved user experience by accurately distinguishing intrinsic preference from coupon-induced uplift.
Traditional CTR prediction models will evolve to incorporate causal frameworks, leading to more robust and debiased performance metrics for marketing interventions.
- · Online advertisers
- · E-commerce platforms
- · AI/ML researchers in advertising
- · Consumers (potentially, through better-targeted ads)
- · Traditional CTR prediction models
- · Ad-tech companies reliant on unrefined CTR metrics
More accurate attribution of marketing campaign effectiveness and optimized budget allocation.
Increased investor confidence in digital advertising due to more reliable performance metrics.
Ethical implications requiring consideration as AI models become more adept at influencing and measuring consumer behavior with high precision.
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Read at arXiv cs.LG