{"title":"Books by Charles Shaw","description":null,"products":[{"product_id":"causal-inference-in-marketing-a-practical-toolkit-for-panel-data","title":"Causal Inference in Marketing: A Practical Toolkit for Panel Data","description":"Causal Inference in Marketing: A Practical Toolkit for Panel Data is a two-volume guide to turning messy marketing panels into credible causal evidence. Written for data scientists, marketing analysts, econometricians, and applied researchers, it connects modern causal inference with the operational realities of advertising, pricing, loyalty, platforms, and marketing effectiveness. The set is distinctive in its design-first treatment of panel data: it begins with estimands, assignment mechanisms, support, and diagnostics before moving to estimators. Readers should be comfortable with regression and applied statistics, but the exposition is built to make the assumptions behind causal claims explicit rather than hidden inside software defaults.   Across the two volumes, readers learn how to choose, implement, diagnose, and report causal designs for marketing measurement. Volume 1 develops the foundations, including potential outcomes, design-based thinking for panels, difference-in-differences, event-study designs, synthetic control, hybrid synthetic control methods, interactive fixed effects, matrix completion, dynamic treatment effects, heterogeneity, interference, and spillovers. Volume 2 extends the toolkit to machine learning for nuisance adjustment and treatment-effect heterogeneity, high-dimensional controls, regularisation, continuous and nonlinear panel models, threats to validity, inference and uncertainty quantification, design diagnostics, and applied marketing workflows. The result is a practical framework for evaluating incrementality, media mix models, geo-experiments, platform data, pricing, promotions, customer lifetime value, retention, and reproducibility while keeping the causal target, data limitations, and business decision aligned.","brand":"Gardners","offers":[{"title":"Default Title","offer_id":57503570493813,"sku":"9781041403876","price":150.0,"currency_code":"GBP","in_stock":true}]},{"product_id":"causal-inference-in-marketing-a-practical-toolkit-for-panel-data-1","title":"Causal Inference in Marketing: A Practical Toolkit for Panel Data","description":"Causal Inference in Marketing: A Practical Toolkit for Panel Data is a two-volume guide to turning messy marketing panels into credible causal evidence. Written for data scientists, marketing analysts, econometricians, and applied researchers, it connects modern causal inference with the operational realities of advertising, pricing, loyalty, platforms, and marketing effectiveness. The set is distinctive in its design-first treatment of panel data: it begins with estimands, assignment mechanisms, support, and diagnostics before moving to estimators. Readers should be comfortable with regression and applied statistics, but the exposition is built to make the assumptions behind causal claims explicit rather than hidden inside software defaults.   Across the two volumes, readers learn how to choose, implement, diagnose, and report causal designs for marketing measurement. Volume 1 develops the foundations, including potential outcomes, design-based thinking for panels, difference-in-differences, event-study designs, synthetic control, hybrid synthetic control methods, interactive fixed effects, matrix completion, dynamic treatment effects, heterogeneity, interference, and spillovers. Volume 2 extends the toolkit to machine learning for nuisance adjustment and treatment-effect heterogeneity, high-dimensional controls, regularisation, continuous and nonlinear panel models, threats to validity, inference and uncertainty quantification, design diagnostics, and applied marketing workflows. The result is a practical framework for evaluating incrementality, media mix models, geo-experiments, platform data, pricing, promotions, customer lifetime value, retention, and reproducibility while keeping the causal target, data limitations, and business decision aligned.","brand":"Gardners","offers":[{"title":"Default Title","offer_id":57503571378549,"sku":"9781041403883","price":110.0,"currency_code":"GBP","in_stock":true}]},{"product_id":"causal-inference-in-marketing-a-practical-toolkit-for-panel-data-2","title":"Causal Inference in Marketing: A Practical Toolkit for Panel Data","description":"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 reporting. The emphasis throughout is on clear estimands, credible identification, practical diagnostics, and recognising when the available data cannot support the causal claim being made.   Key Features:Provides a practitioner-first framework for causal panel design in marketing, including estimands, assignment mechanisms, support, diagnostics, and reporting standards.   Covers the core modern panel toolkit, including difference-in-differences, staggered adoption designs, event studies, synthetic control, augmented synthetic control, synthetic difference-in-differences, interactive fixed effects, and matrix completion.   Includes dedicated treatment of dynamic effects, heterogeneity, interference, and spillovers in advertising, pricing, loyalty, platforms, and marketing effectiveness settings.   Written for data scientists, marketing analysts, econometricians, and applied researchers, this volume is intended for readers who are comfortable with regression and applied statistics and want a rigorous, practical route from marketing panel data to causal evidence. Volume 2 extends the toolkit into machine learning, high-dimensional adjustment, continuous treatments, inference, diagnostics, applications, data systems, reproducibility, and future practice.   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.","brand":"Gardners","offers":[{"title":"Paperback","offer_id":57503581274485,"sku":"9781041386261","price":54.99,"currency_code":"GBP","in_stock":true},{"title":"Hardback","offer_id":57682783863157,"sku":"9781041386285","price":145.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0612\/7193\/3106\/files\/9781041386261.jpg?v=1792511699"},{"product_id":"causal-inference-in-marketing-a-practical-toolkit-for-panel-data-4","title":"Causal Inference in Marketing: A Practical Toolkit for Panel Data","description":"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, reproducibility, and open problems. The emphasis throughout is on applying causal principles under the constraints of real marketing data and real organisational settings.   Key Features:Develops machine-learning and high-dimensional methods for panel data, including orthogonalisation, cross-fitting under panel dependence, heterogeneous treatment effects, policy learning, regularisation, and double selection.   Provides a diagnostics and inference playbook covering pre-trends, placebos, sensitivity analysis, bootstrap and randomisation inference, multiplicity, weak instruments, and uncertainty communication.   Connects advanced causal methods to marketing applications, including media mix models, geo-experiments, platform data, pricing, promotions, customer lifetime value, retention, measurement systems, and reproducible evidence production.   Written for data scientists, marketing analysts, econometricians, and applied researchers, this volume is intended for readers who are comfortable with regression and applied statistics and who want to extend causal design into robust implementation, diagnosis, and reporting. Volume 1 develops the foundations, including potential outcomes, design-based thinking, difference-in-differences, event studies, synthetic control, factor and matrix methods, dynamics, heterogeneity, interference, and spillovers.   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.","brand":"Gardners","offers":[{"title":"Default Title","offer_id":57503689212277,"sku":"9781041399612","price":171.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0612\/7193\/3106\/files\/9781041399612.jpg?v=1786867399"},{"product_id":"causal-inference-in-marketing-a-practical-toolkit-for-panel-data-5","title":"Causal Inference in Marketing: A Practical Toolkit for Panel Data","description":"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, reproducibility, and open problems. The emphasis throughout is on applying causal principles under the constraints of real marketing data and real organisational settings.   Key Features:Develops machine-learning and high-dimensional methods for panel data, including orthogonalisation, cross-fitting under panel dependence, heterogeneous treatment effects, policy learning, regularisation, and double selection.   Provides a diagnostics and inference playbook covering pre-trends, placebos, sensitivity analysis, bootstrap and randomisation inference, multiplicity, weak instruments, and uncertainty communication.   Connects advanced causal methods to marketing applications, including media mix models, geo-experiments, platform data, pricing, promotions, customer lifetime value, retention, measurement systems, and reproducible evidence production.   Written for data scientists, marketing analysts, econometricians, and applied researchers, this volume is intended for readers who are comfortable with regression and applied statistics and who want to extend causal design into robust implementation, diagnosis, and reporting. Volume 1 develops the foundations, including potential outcomes, design-based thinking, difference-in-differences, event studies, synthetic control, factor and matrix methods, dynamics, heterogeneity, interference, and spillovers.   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.","brand":"Gardners","offers":[{"title":"Default Title","offer_id":57503690031477,"sku":"9781041399728","price":63.99,"currency_code":"GBP","in_stock":true}]}],"url":"https:\/\/backstory.london\/collections\/author-charles-shaw.oembed","provider":"Backstory","version":"1.0","type":"link"}