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The Effect: An Introduction to Research Design and Causality
The Effect: An Introduction to Research Design and Causality
is about research design, specifically concerning research that uses observational data to make a causal inference. It is separated into two halves, each with different approaches to that subject. The first half goes through the concepts of causality, with very little in the way of estimation. It introduces the concept of identification thoroughly and clearly and discusses it as a process of trying to isolate variation that has a causal interpretation. Subjects include a heavy emphasis on data-generating processes and causal diagrams.
Concepts are demonstrated with a heavy emphasis on graphical intuition and the question of what we do to data. When we “add a control variable” what does that actually do?
Key Features:
• Extensive code examples in R, Stata, and Python
• Chapters on overlooked topics in econometrics classes: heterogeneous treatment effects, simulation and power analysis, new cutting-edge methods, and uncomfortable ignored assumptions
• An easy-to-read conversational tone
• Up-to-date coverage of methods with fast-moving literature like difference-in-differences
Concepts are demonstrated with a heavy emphasis on graphical intuition and the question of what we do to data. When we “add a control variable” what does that actually do?
Key Features:
• Extensive code examples in R, Stata, and Python
• Chapters on overlooked topics in econometrics classes: heterogeneous treatment effects, simulation and power analysis, new cutting-edge methods, and uncomfortable ignored assumptions
• An easy-to-read conversational tone
• Up-to-date coverage of methods with fast-moving literature like difference-in-differences
619 pages, Paperback
Published January 5, 2022
70 people are currently reading
370 people want to read
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Displaying 1 - 11 of 11 reviews
February 26, 2022
Very useful introduction to causal modeling for non-academics like myself. And surprisingly funny for a statistics textbook! Contains software examples in, impressively, three separate languages (R, Python, Stata). Highly recommended if you're past introductory statistics but are new to the causal framework.
The only reason I give this 4 stars instead of 5 is because the physical copy is poorly adapted (looks like they just printed a PDF of the website). Thankfully, the book is, as of writing this, free in its entirety online. Check it out:
https://theeffectbook.net/
The only reason I give this 4 stars instead of 5 is because the physical copy is poorly adapted (looks like they just printed a PDF of the website). Thankfully, the book is, as of writing this, free in its entirety online. Check it out:
https://theeffectbook.net/
February 1, 2024
One the best beginner’s books for those interested quasi-experimental research (me)!
March 30, 2023
It has been hard to get a good overview of quasi-experimental study designs. Unlike random-control group designs, stemming from medicine, quasi experimental designs stem from different disciplines.
Quasi-experimental study designs aim to tease out causal effects in observational settings. Where random-control group designs have strong internal validity, quasi-experimental designs often have stronger external validity. For those interested, look up "Qausi-experimental study designs - paper 2: Complementary approaches to advancing global health knowledge" By Geldsetzer and Fawzi (2017).
This book first gives a step-by-step introduction of regression methods, and some do-calculus, before progressing to describing thoroughly a large set of quasi-experimental methods. As it often goes, the topic is endless, so although the set is considerable, it is by no means exhaustive; and the author makes no such claim. The tone of the book is very informal (which does not add to brevity), and provides examples in Python, R and Stata.
Causal methods become relevant for data scientists if one wants to move past predictive modeling, and wants to explore what measure effectively influence behavior.
What would have made this book for me perfect is Bayesian modeling. The different designs now rely on a wild scattering of statistical packages in for example Python. A lot of these methods could also be executed using a more general Bayesian package, harmonizing and better illuminating the differences and commonalities between the methods.
All-in-all a very good read.
Quasi-experimental study designs aim to tease out causal effects in observational settings. Where random-control group designs have strong internal validity, quasi-experimental designs often have stronger external validity. For those interested, look up "Qausi-experimental study designs - paper 2: Complementary approaches to advancing global health knowledge" By Geldsetzer and Fawzi (2017).
This book first gives a step-by-step introduction of regression methods, and some do-calculus, before progressing to describing thoroughly a large set of quasi-experimental methods. As it often goes, the topic is endless, so although the set is considerable, it is by no means exhaustive; and the author makes no such claim. The tone of the book is very informal (which does not add to brevity), and provides examples in Python, R and Stata.
Causal methods become relevant for data scientists if one wants to move past predictive modeling, and wants to explore what measure effectively influence behavior.
What would have made this book for me perfect is Bayesian modeling. The different designs now rely on a wild scattering of statistical packages in for example Python. A lot of these methods could also be executed using a more general Bayesian package, harmonizing and better illuminating the differences and commonalities between the methods.
All-in-all a very good read.
December 18, 2024
I enjoyed this book immensely. I stumbled upon this book by accident while looking into some econometric concepts, and I found it very useful. I feel this book provided a thorough description of the key-concepts that underpin econometric pursuits without getting too deep into the mathematical framework. As a current graduate student in economics, I can say having read this book helped me in pursuit of my degree. I personally enjoyed the humor and little asides as well, I felt it added warmth to an otherwise dry subject.
March 25, 2025
This book is a fantastic companion to anyone pursuing something statistics related, and beyond that a great shelf piece that I find myself referring to regularly. Each concept is broken down into bite-sized pieces and intuitive understanding is heavily emphasized. This book got me through undergrad economics and data science courses, helped me refine my skills in R, and most importantly, made learning exciting. I look forward to taking on grad school with this book by my side!
March 7, 2022
Un libro intermedio sobre causalidad y econometría. Algo único del libro es que incluye una discusión extendida sobre diseño de investigación, algo que otros libros rara vez tocan. La segunda mitad del libro es sobre estrategias de identificación. Está muy bien explicado, y creo que debería ser referencia obligada para todos los que estamos interesados en estos temas.
April 20, 2022
What a charming author for such a stringent and knock-down topic
July 19, 2024
The author said that his mom is allergic to statistics, but not the Part I of this book. LOL.
February 22, 2025
Absolutely necessary for anyone interested in understanding the nature of causality and econometrics methods.
June 7, 2023
My 3rd re-read of this book in a year for a reason. This is without a doubt the best introductory textbook on causal inference. But why put it in that box alone? It also serves as a fantastic introduction to basic research design, statistics, and even a bit of machine learning. While this book can be praised for it's breadth, it's true values lies in the author's ability to communicate esoteric concepts and methodologies. He does this better than any other author or lecturer I've seen. I frequently review sections of this book to make sure I'm right on various fundamentals (and I take no shame in this - I naturally speak in English, not LaTeX). And if introductory concepts and solid backgrounds on various causal identification strategies is not enough, I think the conclusion of this book is very sobering and simple. It holds back no punches from the very glaring problems with the enterprise of making causal inferences themselves and it communicates the state of the field in resolving such issues clearly.
February 26, 2025
One of the best educational books I have ever read/studied. The book is written in a manner in which it feels like the author is personally imparting all their knowledge in this domain to you. The math part of the book could have been a little different - it would be much better (for me) if it focused less on code and more on intution, but the research design part of the book is perfectly written. I learned a lot from this book, and this book made the course at my university enjoyable - something that has happened in only one other course (from >50 courses I have taken).
Displaying 1 - 11 of 11 reviews