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Impact Evaluation in Firms and Organizations: With Applications in R and Python by Martin Huber

• Impact Evaluation in Firms and Organizations: With Applications in R and Python
• Martin Huber
• Page: 160
• Format: pdf, ePub, mobi, fb2
• ISBN: 9780262383905
• Publisher: MIT Press

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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

Impact Evaluation In Firms And Organizations:With Applications In R .
Impact Evaluation In Firms and Organizations:With applications In R and Python. [Paperback - 2025]. martin huber. On Demand Pre-Order. Available Around 05-Aug .
Text book "Impact Evaluation in Firms and Organisations"
Text book "Impact Evaluation in Firms and Organisations". Link to MIT Press. Software codes in Python. Python examples version 3.8 .
Impact Evaluation in Firms and Organizations - Apple Books
A comprehensive, nontechnical guide to the methods of data-based impact evaluation in companies and organizations, with coverage of machine learning .
Impact Evaluation in Firms and Organizations by Martin Huber .
With Applications in R and Python | Impact Evaluation in Firms and Organizations by Martin Huber, Paperback | Indigo Chapters.
Martin Huber (@causalhuber.bsky.social) - Bluesky
My new book, Impact Evaluation in Firms and Organizations With Applications in R and Python, will be published on August 5th by @mitpress.bsky.social .
Impact Evaluation in Firms and Organizations - Reston - Scrawl Books
A comprehensive, nontechnical guide to the methods of data-based impact evaluation in companies and organizations, with coverage of machine learning techniques.
Data Processing and Analysis with Applications in Stata, R . - SSRN
This paper describes a range of best practices to compile and analyze datasets, and includes some examples in Stata, R, and Python.
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Impact Evaluation in Firms and Organizations: With Applications in R and Python.
Impact Evaluation in Firms and Organizations: With Applications in R .
PRICES MAY VARY. A comprehensive, nontechnical guide to the methods of data-based impact evaluation in companies and organizations, with coverage of machine .
Impact Evaluation in Firms and Organizations: With Applications in R .
書名：Impact Evaluation in Firms and Organizations: With Applications in R and Python，語言：英文，ISBN：9780262552929，頁數：160，作者：Huber, Martin， .
Impact Evaluation in Firms and Organizations - Apple Books
A comprehensive, nontechnical guide to the methods of data-based impact evaluation in companies and organizations, with coverage of machine learning .

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