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

by Martin Huber

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.

336 pages, Paperback

Published August 1, 2023

Reviews

[Navid Ghorbanali · Rating 4 out of 5]

I read this book shortly after Facure's 'Causal Inference in Python'. While they both cover a lot of the same ground, this is very clearly a more intermediate/advanced text while the former is geared towards beginners. As such, I would not recommend this book for a first dive into Causal Analysis/Inference. The more seasoned readers will enjoy a very up-to-date text with clean and easy to use code - one of the books great strengths. My only gripe is that when reading it, it gave me a strange sense that it was more of a very detailed set of lecture notes rather than a true book. While many readers will surely not share this view, my personal preferences force me to give this book 4 stars rather than a full 5.