Addressing Distribution Shift in RTB Markets via Exponential Tilting

TL;DR

This paper explores how distribution shifts affect binary classification in RTB markets and proposes an importance weighting method, ExTRA, to correct these shifts without needing target labels, validated through simulations.

Contribution

It applies the ExTRA importance weighting algorithm to address distribution shifts in RTB markets, demonstrating its effectiveness in correcting biases without target label data.

Findings

ExTRA effectively estimates importance weights in RTB scenarios.

The method improves model performance under distribution shifts.

Simulation results validate the approach's efficiency.

Abstract

In machine learning applications, distribution shifts between training and target environments can lead to significant drops in model performance. This study investigates the impact of such shifts on binary classification models within the Real-Time Bidding (RTB) market context, where selection bias contributes to these shifts. To address this challenge, we apply the Exponential Tilt Reweighting Alignment (ExTRA) algorithm, proposed by Maity et al. (2023). This algorithm estimates importance weights for the empirical risk by considering both covariate and label distributions, without requiring target label information, by assuming a specific weight structure. The goal of this study is to estimate weights that correct for the distribution shifts in RTB model and to evaluate the efficiency of the proposed model using simulated real-world data.