# Incrementality Bidding and Attribution

arXiv: https://arxiv.org/abs/2208.12809 · HTML: https://mlsanity.com/paper/2208.12809/

## Key facts

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

## Builds on (most cited first, 1 of 1)

- [Estimating Treatment Effects using Multiple Surrogates: The Role of the Surrogate Score and the Surrogate Index](https://mlsanity.com/paper/1603.09326/) (2016)

## Built on by (most cited first, 3 of 3)

- [The (Marginal) Value of a Search Ad: An Online Causal Framework for Repeated Second-price Auctions](https://mlsanity.com/paper/2605.01756/) (2026)
- [The New Era of Dynamic Pricing: Synergizing Supervised Learning and Quadratic Programming](https://mlsanity.com/paper/2402.14844/) (2024)
- [Learning to Bid in Repeated Second-Price Auctions with Dynamic Values and Aggregated Feedback](https://mlsanity.com/paper/2605.28133/) (2026)

## 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.