Effect

Nick Huntington-Klein is a professor of economics at Seattle University specializing in the study of the education system and applied econometrics. He is known as someone who can clearly explain complex topics in econometrics, and his teaching materials have been shared online tens of thousands of times. To his great anguish, his toddler daughter refuses to learn about sampling variation and would rather listen to that one Moana song on repeat.

"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. He has created numerous on-ramps into econometrics that can help hit many different types of students at where they are, rather than teaching to students who most resemble the kind of student that our econometrics and statistics professors were when they were the students’ age. I mean that in the most flattering way possible.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 …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, Baylor University (author of Causal Inference: A Mix Tape)

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