The Effect by Huntington-Klein, Nick;

Short description:

This book is about research design, specifically concerning research that uses non-experimental data to figure out whether one thing causes another. It is separated into two halves, each with different approaches to that subject. Concepts are demonstrated with a heavy emphasis on graphical intuition and the question of what we do to data.

Long description:

The Effect: An Introduction to Research Design and Causality, Second edition is an excellent teaching text 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 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?

The target audience is practitioners as well as undergraduate and graduate students studying causal inference in various fields such as statistics, econometrics, biostatistics, the social sciences and data science. 

Key Features:

• Extensive code examples in R, Stata, and Python


• Chapters on 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 literatures like difference-in-differences


• The second edition features a new chapter on partial identification, updated materials, methods, and writing throughout, and additional materials for help navigating the book or in using the book in teaching.

From the first edition: 

“I think my most useful comment likely comes in the comparison to existing titles. I know that there is a lot going on right now in the causal inference literature, but I do think this author found a unique niche. This book feels far more "solution oriented" and focused not only on teaching these methods but acknowledging and embracing their real-world messiness and limitations to answer real questions. I think this is powered through his outsized coverage of modern techniques / advances and his end-of-chapter examples of these methods being used in real life.” – Emily Riederer, Capitol One

“Nick has created a classic. Can’t say it any other way.  It’s the replacement for Mastering Metrics that we all wanted.  This is the book that will empower students in both understanding what econometrics is or can be, and how to get from A to B with programming practice.  I think the book is phenomenal and will sell well. It’s basically an ambitious book that seeks to take students with zero knowledge of causal inference, but also zero knowledge of programming languages, and possibly even minimal knowledge of statistics, and over 600 pages with excellent writing, extensive programming examples across multiple languages, and causal graphs cover just about everything remotely conceivable to make a student conversant and maybe even competent. Except for my book, there’s nothing like what Nick has done on the market. The publisher that gets to publish it is very lucky. It will be a very popular companion textbook on many econometrics courses, and may even help facilitate the creation of more causal inference courses are all levels. I think Nick has absolutely nailed it.” -Scott Cunningham, author of Causal Inference: A Mixtape

Mastering metrics is a nice book, but has very little depth.  This book has far more depth but is also very accessible.  As such, I think the book fills a very real need. – Luke Keele, University of Pennsylvania

”I think this book would do astoundingly well in undergrad economics courses (especially in those courses attempting to cater to a broad audience). The key competition in this space would be mastering metrics and this text brings a very unique new perspective on it – I think more math averse students would particularly benefit from this. Book is very cool.” – Paul Goldsmith-Pinkham, Yale School of Management

"A must-read for all epidemiologists and biostatisticians, due to its coverage of key principles of causal inference. Therefore, thisbook may be recommended to any methodologist in the field of health research, who strives to gain a comprehensive understanding of causal inference theoretically, and the statistical skillset to answer research questions using observational data."

Myanca Rodrigues, Canada, ISCB News, June 2022.

"The Effect is a gentle introduction to causality and research design which is accessible to a wide audience. By intent, thebook does not overload the reader with formal notation ormathematics. Instead, the author, Nick Huntington-Klein,builds intuition through helpful examples and plots"

Y. Samuel Wang, USA, Data Science in Science, February 2023.

"The author clearly has achieved the goal of providing an accessible introduction to causality. Any newcomer to causal inference would benefit from reading this book. Huntington–Klein’s conversational delivery and avoidance of explicit mathematics in the first half of the text provides the reader with the building blocks to causally reason about a system. The second part strives to make technical tools accessible, and the code examples make these tools readily available for readers to try on their own data. This textbook will be a useful addition to the library of anyone studying causal discovery and inference."

Hung-Ching Chang and Muchael T. Gorczyca, Biometrics: A Journal of the International Biometric Society, 2023.

"Overall, this book, though very voluminous, is an excellent addition to the world of literature. The book contains a good number of examples and wonderfully drawn diagrams, that facilitate a clearer understanding of the concepts. It is a wonderful exhibition of the parts and parcels of research design and causality."

Nisar Ahmad Khan, India, Technometrics, April 2023.

"A great textbook for an undergraduate introductory data science course or social science methodology course as well as a reference for beginning graduate students. It would also benefit researchers who are working with data but are wholly clear about where to start when investigating causal relationships."

Brian W. Sloboda, University of Maryland, USA, International Statistical Review, 2023.

Table of Contents:

Introduction   Finding Stuff in this Book   Part I. The Design of Research   1 Designing Research   2 Research Questions   3 Describing Variables   4 Describing Relationships   5 Identification   6 Causal Diagrams   7 Drawing Causal Diagrams   8 Causal Paths and Closing Back Doors   9 Finding Front Doors   10 Treatment Effects   11 Causality with Less Modeling   Part II. The Toolbox   12 Opening the Toolbox   13 Regression   14 Matching   15 Simulation   16 Fixed Effects   17 Event Studies   18 Difference-in-Differences   19 Instrumental Variables   20 Regression Discontinuity   21 Partial Identification   22 A Gallery of Rogues: Other Methods   23 Under the Rug   Bibliography   Index