experiment-design

TL;DR Abadie and Zhao propose using synthetic control methods to design experiments when only one or a few large aggregate units (e.g., markets, cities) can be treated. Rather than randomizing treatment assignment—which can produce large post-randomization biases with small samples—their method jointly selects which units receive treatment and which serve as controls by matching pre-treatment characteristics.

1. Introduction This guide provides practical techniques for increasing the statistical power of marketing experiments without relying solely on large sample sizes. Based on Meyvis and van Osselaer’s work (2018), these methods enable researchers to detect subtle marketing effects with feasible sample sizes by increasing observed effect sizes through proper design and analysis decisions.