A comprehensive, nontechnical guide to the methods of data-based impact evaluation in companies and organizations, with coverage of machine learning techniques.
In today's dynamic business climate, organizations face the constant challenge of making informed decisions about their interventions, from marketing campaigns and pricing strategies to employee training programs. In this practical textbook, Martin Huber provides a concise but comprehensive guide to quantitatively assessing the impact of such efforts, enabling decision-makers to make evidence-based choices.
The book introduces fundamental concepts, emphasizing the importance of causal analysis in understanding the true effects of interventions, before detailing a wide range of quantitative methods, including experimental and nonexperimental approaches. Huber then explores the integration of machine learning techniques for impact evaluation in the context of big data, sharing cutting-edge tools for data analysis. Centering real-world, global applications, this accessible text is an invaluable resource for anyone seeking to enhance their decision-making processes through data-driven insights.
- Highlights the relevance of AI and equips readers to leverage advanced analytical techniques in the era of digital transformation
- Is ideal for introductory courses on impact evaluation or causal analysis
- Covers A/B testing, selection-on-observables, instrumental variables, regression discontinuity designs, and difference-in-differences
- Features extensive examples and demonstrations in R and Python
- Suits a wide audience, including business professionals and students with limited statistical expertise
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978-0-262-38390-5 (9780262383905)
Schweitzer Klassifikation
Martin Huber is Professor of Applied Econometrics at the University of Fribourg, Switzerland, where his research comprises both methodological and applied contributions in the fields of causal analysis and impact evaluation, machine learning, statistics, econometrics, empirical economics, and business analytics. He is the author of Causal Analysis: Impact Evaluation and Causal Machine Learning with Applications in R (MIT Press).
