Causal Inference in Marketing: A Practical Toolkit for Panel Data: Foundations, Core Panel Designs, and Spillovers, Volume 1

The global advertising market is roughly US$1.1 trillion, before accounting for the wider investments firms make in pricing, promotions, loyalty, and customer acquisition.Yet the evidence used to measure these investments is often fragile.Traditional marketing mix models offer operational convenience, while modern econometric methods promise stronger causal identification.In practice, marketing teams must work with short panels, staggered rollouts, overlapping campaigns, platform interference, and competitive responses that make simple before-and-after comparisons unreliable. Volume 1 of Causal Inference in Marketing: A Practical Toolkit for Panel Data develops the foundations and core panel designs needed to turn those messy data structures into credible causal evidence.Grounded in potential-outcomes reasoning and design-based thinking, it translates causal inference into the language of marketing measurement: incrementality, attribution, budget allocation, and decision-relevant repor