Mastering 'Metrics: The Path from Cause to Effect

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This valuable book connects the dots between mathematical formulas, statistical methods, and real-world policy analysis. Reading it is like overhearing a conversation between two grumpy old men who happen to be economists—and I mean this in the best way possible."—Andrew Gelman, Columbia University Few fields of statistical inquiry have seen faster progress over the last several decades than causal inference. With an engaging, insightful style, Angrist and Pischke catch readers up on five powerful methods in this area. If you seek to make causal inferences, or understand those made by others, you will want to read this book as soon as possible."--Gary King, Harvard University We don't want a book which gives us examples and then loses us in these examples. For examples should lead us to building of the concepts and continue our quest forward. "

Written by true 'masters of 'metrics, ' this book is perfect for those who wish to study this important subject. Using real-world examples and only elementary statistics, Angrist and Pischke convey the central methods of causal inference with clarity and wit."--Hal Varian, chief economist at Google 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. But to those who want to understand what most economists do these days and what are their methods - I think this book is a neat introduction. Angrist, Joshua D. & Pischke, Jörn-Steffen (2015). Mastering 'Metrics: The path from cause to effect. Princeton, Oxford: Princeton University Press, 304 p., 35 USD, ISBN 978-0-691-15284-4So i have almost reached halfway chapter 4 where RDD is being discussed. I found the chapters imbalanced. Like the IV chapter was very heavy and was not a smoother flow like the other ones. Regression Discontinuity Designs 147 4.1 Birthdays and Funerals 148 4.2 The Elite Illusion 164 Masters of 'Metrics: Donald Campbell 175 Posing several well-chosen empirical questions in social science, Mastering 'Metrics develops methods to provide the answers and applies them to interesting datasets. This book will motivate beginning students to understand econometrics, with an appreciation of its strengths and limits."—Gary Chamberlain, Harvard University But the IV chapter was better in terms of the details whereas RDD chapter isn't as heavy on those details. So the detailing level has to be consistent. Further there is a need to link the discussions. Suddenly a topic is completed and another section starts with a new topic. This to me seems disconnected and you don't really get the flow in the argument while reading the book.

Around five years ago, Joshua D. Angrist and Jörn-Steffen Pischke published their first joint book on econometrics tools for causal inference: Mostly harmless econometrics (2009). Although this book is excellent in many regards (e.g., more than 5000 quotes on Google Scholar), it was not as harmless as the title might suggest. Mastering 'Metrics: The path from cause to effect now fills this gap, as it is a truly nontechnical introduction. Regression 47 2.1 A Tale of Two Colleges 47 2.2 Make Me a Match, Run Me a Regression 55 2.3 Ceteris Paribus? 68 Masters of 'Metrics: Galton and Yule 79 Appendix: Regression Theory 82 Wielding econometric tools with skill and confidence, Mastering 'Metrics uses data and statistics to illuminate the path from cause to effect.Data scientists, on the other hand, don't often think about economics at all. From their perspective the two disciplines have basically no overlap. So they struggle to see why they should care about what an economist has to say about anything. This is primarily driven by the popular misperception of economics being about business questions. Imagine their frustration when economists start telling them that their results are wrong.

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about. You have requested "on-the-fly" machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Show full disclaimer Posing several well-chosen empirical questions in social science, "Mastering 'Metrics" develops methods to provide the answers and applies them to interesting datasets. This book will motivate beginning students to understand econometrics, with an appreciation of its strengths and limits."--Gary Chamberlain, Harvard University Angrist, JD, and J-S Pischke (2015), Mastering Metrics: The Path from Cause to Effect, Princeton University Press. Or have you wondered why we have to measure weird things (data on quarter of births) to understand the impact of education. These and many other issues which are explored in this book actually bring out a glamorous aspect of the toils economists go through in examining an issue with the precision, care and concern - especially because policies are a result of these studies! It is thus an intersting starting place for beginners too! However, my expectations from this book were more - especially since I like the papers written by Angrist etc.

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The disconnect between econometric teaching and econometric practice goes beyond questions of tone and illustration. The most disturbing gap here is conceptual. The ascendance of the five core econometric tools – experiments, matching and regression methods, instrumental variables, differences-in-differences and regression discontinuity designs – marks a paradigm shift in empirical economics. In the past, empirical research focused on the estimation of models, presented as tests of economic theories or simply because modelling is what econometrics was thought to be about. Contemporary applied research asks focussed questions about economic forces and economic policy.

In our experience, most econometrics teachers enjoy working with data, and they hope and expect that their students will too. Yet, a sad consequence of the inherited econometrics canon is its drabness. This is really too bad because modern applied econometrics is interesting, relevant, and, yes, fun! Instructors who have as much fun teaching econometrics as they do when they use it in their research can hope to transmit their excitement to their students. In addition to having a good time, we plant the seeds of useful data analysis in the next generation of scholars, policy-makers, and an economically literate citizenry. The promise of our approach to instruction is evident in the popularity of the Freakonomics franchise and in the sparkling new intro-to-economics principles book by Acemoglu, Laibson, and List (2015): their take on economics puts questions and evidence ahead of abstract models. We’re happy to join these colleagues in an effort to polish and renew our profession’s rusty instructional canon. The five most valuable econometric methods, or what the authors call the Furious Five—random assignment, regression, instrumental variables, regression discontinuity designs, and differences in differences—are illustrated through well-crafted real-world examples (vetted for awesomeness by Kung Fu Panda’s Jade Palace). Does health insurance make you healthier? Randomized experiments provide answers. Are expensive private colleges and selective public high schools better than more pedestrian institutions? Regression analysis and a regression discontinuity design reveal the surprising truth. When private banks teeter, and depositors take their money and run, should central banks step in to save them? Differences-in-differences analysis of a Depression-era banking crisis offers a response. Could arresting O. J. Simpson have saved his ex-wife’s life? Instrumental variables methods instruct law enforcement authorities in how best to respond to domestic abuse. The writing is lively and engaging, with quotes, anecdotes and jokes scattered throughout. . . . I have become a big fan of this new textbook. . . . In my view, the emphasis on thinking about parameters of interest and identification before discussing technical matters is a huge improvement on traditional teaching approaches. 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."—Tue Gørgens, Economic Record The first chapter Randomized Trials outlines basic experimental concepts like treatment, outcome, control and treatment group, the fundamental problem that we can always only observe one reality in one person, and the idea that randomization makes "other things equal" (p. xii). It also points out why perfect randomization is difficult to achieve in real life. Furthermore, the issue of statistical significance in the interpretation of results is discussed, as analyses are usually only based on samples drawn from populations. The fact that there are not endless instrumental variables given in all areas of interest, often makes it necessary to use other approaches like Differencesin-Differ enees, which is illustrated in chapter 5. The authors explain how developments of control and treat- ment groups can indicate treatment effects, even in the absence of randomization. The approach assumes that even if groups differ in the outcome from the very beginning, a non-parallel development of the groups can be attributed to the treatment, which is again illustrated clearly using econometric examples.

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With humor and rigor, this book explores key approaches in applied econometrics. The authors present accessible, interesting examples—using data-heavy figures and graphic-style comics—to teach practitioners the intuition and statistical understanding they need to become masters of 'metrics. A must-read for anyone using data to investigate questions of causality!"—Melissa S. Kearney, University of Maryland and the Brookings Institution Modern econometrics is more than just a set of statistical tools--causal inference in the social sciences requires a careful, inquisitive mindset. "Mastering 'Metrics" is an engaging, fun, and highly accessible guide to the paradigm of causal inference."--David Deming, Harvard University