000			02351nam a22002177a 4500
005			20251124100006.0
008			251124b |||||||| |||| 00| 0 eng d
020			_a9780262552929
028			_a: Samyak Publisher _b: Ghaziabad
082			_a658.4 HUB
100			_aHuber, Martin.
245			_aImpact Evaluation in Firms and Organizations _b: With Applications in R and Python. _c/ Martin Huber.
260			_a, London _b: The MIT Press _c, 2025.
300			_avii, 144p. _c: 24cm.
520			_aA 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
650			_aImpact Evaluation
650			_aApplications in R and Python
650			_aMachine learning techniques
700			_aMartin Huber.
942			_cBOOK
999			_c66632 _d66632