mlsanityData to 7 Oct 2026

Incrementality Bidding and Attribution

arXiv 2208.12809 ↗First posted 25 Aug 2022cs.LGcited by 3

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Abstract (arXiv)

The causal effect of showing an ad to a potential customer versus not, commonly referred to as "incrementality", is the fundamental question of advertising effectiveness. In digital advertising three major puzzle pieces are central to rigorously quantifying advertising incrementality: ad buying/bidding/pricing, attribution, and experimentation. Building on the foundations of machine learning and causal econometrics, we propose a methodology that unifies these three concepts into a computationally viable model of both bidding and attribution which spans the randomization, training, cross validation, scoring, and conversion attribution of advertising's causal effects. Implementation of this approach is likely to secure a significant improvement in the return on investment of advertising.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 1 of its 14 references that are in the map. First posted on arXiv 25 Aug 2022.

Builds on

1 of 14 references.

References · 1

  1. 2016Estimating Treatment Effects using Multiple Surrogates: The Role of the Surrogate Score and the Surrogate Indexcited by 12

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 2.

3 papers.