Mastering 'Metrics: The Path from Cause to Effect

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But where it really stands out is making it personable, drawing you into the book and helping you engage in a learning process that is quite steep. Mastering ‘Metrics is an engaging, fun, and highly accessible guide to the paradigm of causal inference.

In the fourth section, the authors cover how breaks and discontinuities offer opportunities for uncovering useful information.

There is nothing wrong with having an accessible primer on the intuition behind an idea, but if you're doing that you should probably be upfront about it and make recommendations on where to go from here.

Showing how masters from around the world use MM to light their students’ way on the path from cause to effect. Wonderful companion to anyone working through an introductory Python or R course, as the technical implementations would complement this book's explanations well. Written by true 'masters of 'metrics, ' this book is perfect for those who wish to study this important subject. But the IV chapter was better in terms of the details whereas RDD chapter isn't as heavy on those details.

If you seek to make causal inferences, or understand those made by others, you will want to read this book as soon as possible. While the book caters to a rather niche audience, I appreciated the enthusiasm and humor the authors sprinkle throughout the pages to make the material accessible. This leads easily to a discussion of two-stage least squares as a powerful method harnessing variation in instruments originating from a wide variety of sources. There is definitely an art to econometrics, but clouding econometrics in mysticism does more to protect the reputation of the teacher than it does to advance the student's learning. In other words, what would you like to hold fixed when trying to regress-out an average causal effect?

Um dos autores, Joshua (Nobel) possui um canal no youtube com o mesmo assunto e explica bem as ferramentas de inferência causal de uma forma bem lúdica. Focusing on digging into policy studies, understanding and deconstructing statistical tables, and navigating the precise language to be used when discussing and explaining data, validity, and results to others. Many of you are probably thinking that statement doesn't set a very high bar, because most econometrics books are drier than a particular arid corner of the Sahara, and almost impenetrable to anyone without high-level mathematics skills. Our focus on five core econometric tools is a natural consequence of contemporary econometric practice, which owes little to the formalities of the classical linear regression model, the arcane statistical assumptions of generalised least squares, or the elaborate simultaneous equations framework that fill so many texts.

These cases include factors in the success of charter schools, drivers of SAT scores, and bank failures during the Great Depression. I would be hard pressed to name another econometrics book that can be read for enjoyment yet provides useful quantitative insights. While very readable and conversational in tone, it is still rather terse in some sections as if you're expected to already have seen most of the material here on a previous introductory course. Instructors may have to spend more time preparing lectures and tutorials, but I predict significant benefits in terms of students' learning and appreciation of applied econometrics. Modern econometrics is more than just a set of statistical tools–causal inference in the social sciences requires a careful, inquisitive mindset.

The positives of this book are instantly revealed to those who are working on this topic, so for them I am not going to comment much.A breezy presentation of very general topics, it is nevertheless packed with good, commonsense advice on how to perform econometric analysis.