# Trimmed Match Design for Randomized Paired Geo Experiments

**Authors:** Aiyou Chen, Marco Longfils, Nicolas Remy  
**arXiv:** [2105.07060](https://arxiv.org/abs/2105.07060)  
**Date:** 2021-05-14  
**Summary page:** https://www.summarizepaper.com/fr/arxiv-id/2105.07060v2/

## Abstract

How to measure the incremental Return On Ad Spend (iROAS) is a fundamental problem for the online advertising industry. A standard modern tool is to run randomized geo experiments, where experimental units are non-overlapping ad-targetable geographical areas (Vaver & Koehler 2011). However, how to design a reliable and cost-effective geo experiment can be complicated, for example: 1) the number of geos is often small, 2) the response metric (e.g. revenue) across geos can be very heavy-tailed due to geo heterogeneity, and furthermore 3) the response metric can vary dramatically over time. To address these issues, we propose a robust nonparametric method for the design, called Trimmed Match Design (TMD), which extends the idea of Trimmed Match (Chen & Au 2019) and furthermore integrates the techniques of optimal subset pairing and sample splitting in a novel and systematic manner. Some simulation and real case studies are presented. We also point out a few open problems for future research.