Causal Models for Real Time Bidding with Repeated User Interactions

Martin Bompaire (1) , Alexandre Gilotte (1) , Benjamin Heymann (1)

1 Criteo AI Lab

A large portion of online advertising displays are sold through an auction mechanism called Real Time Bidding (RTB). Each auction corresponds to a display opportunity, for which the competing advertisers need to precisely estimate the economical value in order to bid accordingly. This estimate is typically taken as the advertiser's payoff for the target event - such as a purchase on the merchant website attributed to this display - times this event estimated probability. However, this greedy approach is too naive when several displays are shown to the same user. The purpose of the present paper is to discuss how such an estimation should be made when a user has already been shown one or more displays. Intuitively, while a user is more likely to make a purchase if the number of displays increases, the marginal effect of each display is expected to be decreasing. In this work, we first frame this bidding problem with repeated user interactions by using causal models to value each display individually. Then, based on this approach, we introduce a simple rule to improve the value estimate. This change shows both interesting qualitative properties that follow our previous intuition as well as quantitative improvements on a public data set and online in a production environment.

Mots clés: attribution; incrementality; causality; real-time bidding

Domaines: Machine Learning [stat.ML]

Martin Bompaire, Alexandre Gilotte, Benjamin Heymann. Causal Models for Real Time Bidding with Repeated User Interactions. 2021. ⟨hal-02971865v2⟩