English

Incrementality Bidding and Attribution

Machine Learning 2022-08-30 v1

Abstract

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.

Keywords

Cite

@article{arxiv.2208.12809,
  title  = {Incrementality Bidding and Attribution},
  author = {Randall Lewis and Jeffrey Wong},
  journal= {arXiv preprint arXiv:2208.12809},
  year   = {2022}
}

Comments

40 pages

R2 v1 2026-06-25T02:00:57.095Z