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Martin Huber

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

by Martin Huber (Author) Format: Kindle Edition

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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

Print length

318 pages
Language

English
Publisher

The MIT Press
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Publication date

Aug. 1 2023
File size

24.8 MB
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Causal Inference and Discovery in Python: Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more
Aleksander Molak

Kindle Edition
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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)
- #3 in Professional & Technical Econometrics
- #3 in Econometrics (Kindle Store)
- #21 in Econometrics in Finance
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12 ratings

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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
Excellent overview of contemporary causal inference
Reviewed in the United States on August 6, 2023
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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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