1st Edition
Causal Inference in Marketing: A Practical Toolkit for Panel Data Two-Volume Set
Part 1: Foundations 1. Why Marketing Panel Data Need Causal Design 2. Causal Frameworks and Panel Notation 3. Design-Based Thinking for Panels Part 2: Differences-in-Differences and Event Studies 4. Difference-in-Differences: From Canonical to Staggered 5. Event-Study Designs Part 3: Synthetic Controls and Hybrid Methods 6. Synthetic Control 7. Hybrid Synthetic Control Methods Part 4: Factor Models and Matrix Methods 8. Interactive Fixed Effects and Matrix Completion 9. Advanced Matrix Methods for Causal Inference Part 5: Dynamics, Heterogeneity, and Spillovers 10. Dynamic Treatment Effects 11. Interference and Spillovers Part 6: Machine Learning and High-Dimensional Methods 12. Machine Learning for Nuisance and Heterogeneity 13. High-Dimensional Controls and Regularisation 14. Continuous and Nonlinear Panel Models Part 7: Validity, Inference, and Diagnostics 15. Threats to Validity in Marketing Panels 16. Inference and Uncertainty Quantification 17. Design and Diagnostics Part 8: Applications and Future Directions 18. Applications in Marketing 19. Measurement, Platform Data, and Reproducibility 20. Outlook and Open Problems Part 9: Appendices A. Time Series: Recap of Basic Principles B. Stationarity and Cointegration in Panels
Biography
Charles Shaw is a Data Science Director at WPP Media, where he leads econometric measurement and optimisation for global brands. His work focuses on causal inference, econometric measurement, Bayesian modelling, machine learning, and marketing effectiveness. He develops applied frameworks for privacy-constrained attribution, media incrementality, platform effects, dynamic pricing, and scalable causal workflows in commercial settings.
