Pseudo-strata learning via maximizing misclassification reward

TL;DR

This paper introduces a novel method for learning pseudo-strata in online advertising by maximizing misclassification rewards, leading to improved revenue without needing to identify true user response types.

Contribution

It proposes a Bayesian classification approach that learns pseudo-strata using outcome data, with new identification assumptions and estimation methods for revenue optimization.

Findings

Achieves more accurate strata classification

Substantially higher revenue in simulations

Effective on large-scale industrial data

Abstract

Online advertising aims to increase user engagement and maximize revenue, but users respond heterogeneously to ad exposure. Some users purchase only when exposed to ads, while others purchase regardless of exposure, and still others never purchase. This heterogeneity can be characterized by latent response types, commonly referred to as principal strata, defined by users' joint potential outcomes under exposure and non-exposure. However, users' true strata are unobserved, making direct analysis infeasible. In this article, instead of learning the true strata, we propose a novel approach that learns users' pseudo-strata by leveraging information from an outcome (revenue) observed after the response (purchase). We construct pseudo-strata to classify users and introduce misclassification rewards to quantify the expected revenue gain of pseudo-strata-based policies relative to true strata. Within a Bayesian classification framework, we learn the pseudo-strata by optimizing the expected revenue. To implement these procedures, we introduce identification assumptions and estimation methods, and establish their large-sample properties. Simulation studies show that the proposed method achieves more accurate strata classification and substantially higher revenue than baselines. We further illustrate the method using a large-scale industrial dataset from the Criteo Predictive Search Platform.