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  • Causal Analysis: Impact Evaluation and Causal Machine Learning with Applications in R
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Causal Analysis: Impact Evaluation and Causal Machine Learning with Applications in R Kindle Edition

4.4 out of 5 stars 12 ratings

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A comprehensive and cutting-edge introduction to quantitative methods of causal analysis, including new trends in machine learning.

Reasoning about cause and effect—the consequence of doing one thing versus another—is an integral part of our lives as human beings. In an increasingly digital and data-driven economy, the importance of sophisticated causal analysis only deepens.  Presenting the most important quantitative methods for evaluating causal effects, this textbook provides graduate students and researchers with a clear and comprehensive introduction to the causal analysis of empirical data. Martin Huber’s accessible approach highlights the intuition and motivation behind various methods while also providing formal discussions of key concepts using statistical notation.
Causal Analysis covers several methodological developments not covered in other texts, including new trends in machine learning, the evaluation of interaction or interference effects, and recent research designs such as bunching or kink designs.

  • Most complete and cutting-edge introduction to causal analysis, including causal machine learning 
  • Clean presentation of rigorous material avoids extraneous detail and emphasizes conceptual analogies over statistical notation
  • Supplies a range of applications and practical examples using R
Due to its large file size, this book may take longer to download

Product description

About the Author

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 policy evaluation, machine learning, statistics, econometrics, and empirical economics.

Product details

  • ASIN ‏ : ‎ B0BL68LXM2
  • Publisher ‏ : ‎ The MIT Press
  • Accessibility ‏ : ‎ Learn more
  • Publication date ‏ : ‎ Aug. 1 2023
  • Language ‏ : ‎ English
  • File size ‏ : ‎ 24.8 MB
  • Enhanced typesetting ‏ : ‎ Enabled
  • X-Ray ‏ : ‎ Not Enabled
  • Word Wise ‏ : ‎ Not Enabled
  • Print length ‏ : ‎ 318 pages
  • ISBN-13 ‏ : ‎ 978-0262374927
  • Page Flip ‏ : ‎ Enabled
  • 鶹 Rank: #172,024 in Kindle Store (See Top 100 in Kindle Store)
  • Customer Reviews:
    4.4 out of 5 stars 12 ratings

About the author

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Martin Huber
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Martin Huber is a Professor in the Economics Department at the University of Fribourg (Switzerland). His research focuses on data-based methodologies in statistics, econometrics, causal analysis, impact evaluation, and machine learning. He applies these methodologies to practical settings within empirical economics, covering areas such as labor, health, and education economics, as well as business analytics, including marketing. His teaching expertise spans diverse audiences, from undergraduate and graduate students to Ph.D. candidates and executives.

Martin Huber earned his Ph.D. in Economics and Finance, specializing in econometrics, from the University of St. Gallen (Switzerland) in 2010. Following his doctoral studies, he held the position of Assistant Professor of Quantitative Methods in Economics at the same institution. He conducted a visiting fellowship at Harvard University in 2011–2012 and assumed the role of Professor of Applied Econometrics and Policy Evaluation at the University of Fribourg (Switzerland) in 2014.

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  • Patrick
    5.0 out of 5 stars Excellent overview of contemporary causal inference
    Reviewed in the United States on August 6, 2023
    Verified Purchase
    This is an excellent overview of modern causal inference, with broad coverage and more emphasis on semiparametric/nonparametric techniques than competing "intro to causal inference" books.

    The highlight for me was the chapter on causal machine learning. Huber's explanations are very clear, and I know of no other book that treats this (quite trendy) topic. The references throughout are extremely up to date; a significant fraction are from the mid-2010s are later, and even some important preprints from 2022 are cited. Some other topics it discusses that I haven't seen in other introductory books include sensitivity analysis, partial identification, partial inference, and exposure mappings. Besides that, it covers all the usual things, including matching, propensity scores, differences in differences, regression discontinuity, synthetic control, etc.

    The book is written as a survey. The author gives precise mathematical statements of key facts, but proofs are not given (except for a regression review, and short arguments in later chapters); the reader is referred to other sources for the details. You need to be familiar with the basics of probability and regression before reading this book; it is not suitable for someone who has never taken a class in probability or statistics. But the prerequisites are relatively mild. It should be accessible to well-prepared 3rd/4th year undergraduates and master's students. (The regression math in the sample pages is the hardest in the book - note that you need to look at chapter 3 in the Kindle preview, not the paperback preview, which is more limited.) R code is provided for examples.

    This is a fantastic book and I strongly recommend it.

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