🤖 AI Summary
This study addresses the pervasive issue of attribution bias in upper-funnel advertising, where incremental campaign effects are often misattributed to downstream channels—a phenomenon colloquially termed “assist miscredit”—leading to distorted ROAS metrics and flawed marketing mix models. To resolve this, the authors propose a novel individual-level incrementality-based measurement framework that embeds an intent-to-treat (ITT) experiment within real-world ad-exposed audiences. By extending the PIE (Probabilistic Impact Estimation) framework to the individual level and integrating audience-level natural randomization with machine learning–based response mapping, the method enables unbiased estimation of each channel’s true causal contribution to conversions. Crucially, it preserves the full conversion path while eliminating attribution bias, thereby delivering granular, actionable insights for optimal budget allocation.
📝 Abstract
We use the assisted own goal hypothesis as a lens into media measurement. A demand-generating (upper-funnel) advertising platform such as a short-video social network can cause an incremental purchase, yet see that purchase booked on -- and credited to -- a downstream trusted marketplace, because consumers who discover a product on the platform complete the transaction elsewhere, for example because of distrust of the generating platform as a psychological mechanism. Under attribution-based return-on-ad-spend (ROAS) measurement, the diverted conversions are invisible to the originating platform. Marketing-mix models (MMMs) do not know which channel to credit with the outcome, and channel-by-week aggregation denies the audience-level granularity that budget decisions require. We develop an incrementality-based measurement model with two ingredients: ambient audience-level randomization -- each activated audience carries its own intent-to-treat (ITT) experiment -- and an individual-level extension of Predicted Incrementality by Experimentation (PIE), which learns a mapping from individual features to experiment-identified incremental outcomes. Because ITT contrasts are computed on channel-complete outcomes, the estimator is unbiased and the own goal disappears