Causal Inference in Marketing: A Practical Toolkit for Panel Data: Machine Learning, Diagnostics, Applications, and Outlook, Volume 2

The global advertising market is roughly US$1.1 trillion, with digital channels accounting for most of that activity.Marketing measurement therefore increasingly depends on complex data environments: high-dimensional covariates, machine-learning systems, continuous treatments, platform reporting constraints, and organisational pressure to turn evidence into decisions.These settings create opportunities for richer causal analysis, but they also raise difficult questions about validity, uncertainty, diagnostics, reproducibility, and whether an estimated effect is useful for the decision at hand. Volume 2 of Causal Inference in Marketing: A Practical Toolkit for Panel Data carries the framework of Volume 1 into the advanced and operational half of the book.It extends the core panel toolkit into machine learning, high-dimensional adjustment, continuous and nonlinear treatment settings, threats to validity, inference, diagnostics, applied marketing workflows, data and measurement systems, r