{"product_id":"impact-evaluation-in-firms-and-organizations-isbn-9780262552929","title":"Impact Evaluation in Firms and Organizations","description":"\u003cb\u003eA comprehensive, nontechnical guide to the methods of data-based impact evaluation in companies and organizations, with coverage of machine learning techniques.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eIn 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.\u003cbr\u003e\u003cbr\u003eThe 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.\u003cbr\u003e\u003cbr\u003e\u003cul\u003e\n\u003cli\u003eHighlights the relevance of AI and equips readers to leverage advanced analytical techniques in the era of digital transformation\u003c\/li\u003e\n\u003cli\u003eIs ideal for introductory courses on impact evaluation or causal analysis\u003c\/li\u003e\n\u003cli\u003eCovers A\/B testing, selection-on-observables, instrumental variables, regression discontinuity designs, and difference-in-differences\u003c\/li\u003e\n\u003cli\u003eFeatures extensive examples and demonstrations in R and Python\u003c\/li\u003e\n\u003cli\u003eSuits a wide audience, including business professionals and students with limited statistical expertise\u003c\/li\u003e\n\u003c\/ul\u003e1 Introduction\u003cbr\u003e2 Basics of impact evaluation\u003cbr\u003e2.1 The fundamental problem of impact evaluation\u003cbr\u003e2.2 Analyzing the impact: characterization and assessment\u003cbr\u003e2.3 The problem of comparing apples to oranges\u003cbr\u003e3 Experiments (A\/B testing)\u003cbr\u003e3.1 Comparing apples to apples\u003cbr\u003e3.2 Behavioral assumptions and methods for analyzing experiments\u003cbr\u003e3.3 Multiple interventions\u003cbr\u003e3.4 Use cases in R\u003cbr\u003e3.5 Use cases in Python\u003cbr\u003e4 Selection on observables: aim to compare apples with apples\u003cbr\u003e4.1 Making groups comparable in observed characteristics\u003cbr\u003e4.2 Behavioral assumptions\u003cbr\u003e4.3 Methods for impact evaluation\u003cbr\u003e4.4 Use cases in R\u003cbr\u003e4.5 Use cases in Python\u003cbr\u003e5 Causal machine learning\u003cbr\u003e5.1 Motivating causal machine learning\u003cbr\u003e5.2 Elements of causal machine learning\u003cbr\u003e5.3 A brief introduction to several machine learning algorithms\u003cbr\u003e5.4 Effect heterogeneity and optimal policy learning\u003cbr\u003e5.5 Use cases in R\u003cbr\u003e5.6 Use cases in Python\u003cbr\u003e6 Instrumental variables\u003cbr\u003e6.1 Instruments and complier effects\u003cbr\u003e6.2 Behavioral assumptions\u003cbr\u003e6.3 Use cases in R\u003cbr\u003e7 Use cases in Python\u003cbr\u003e8 Regression discontinuity designs\u003cbr\u003e8.1 Sharp and fuzzy regression discontinuity designs\u003cbr\u003e8.2 Behavioral assumptions and methods\u003cbr\u003e8.3 Use cases in R\u003cbr\u003e8.4 Use cases in Python\u003cbr\u003e9 Difference-in-Differences\u003cbr\u003e9.1 Difference-in-Differences and the impact in the treatment group\u003cbr\u003e9.2 Behavioral assumptions and extensions\u003cbr\u003e9.3 Use cases in R\u003cbr\u003e9.4 Use cases in Python\u003cbr\u003e10 Synthetic controls\u003cbr\u003e10.1 Impact evaluation when a single unit receives the intervention\u003cbr\u003e10.2 Behavioral assumptions and variants\u003cbr\u003e10.3 Use cases in R\u003cbr\u003e11 Use cases in Python\u003cbr\u003e12 ConclusionMartin 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 \u003ci\u003eCausal Analysis: Impact Evaluation and Causal Machine Learning with Applications in R \u003c\/i\u003e(MIT Press).","brand":"The MIT Press","offers":[{"title":"Default Title","offer_id":48233265529061,"sku":"NP9780262552929","price":40.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780262552929.jpg?v=1767729885","url":"https:\/\/k12savings.com\/products\/impact-evaluation-in-firms-and-organizations-isbn-9780262552929","provider":"K12savings","version":"1.0","type":"link"}