Mastering 'Metrics: The Path from Cause to Effect

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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 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 From Joshua Angrist, winner of the Nobel Prize in Economics, and Jörn-Steffen Pischke, an accessible and fun guide to the essential tools of econometric research

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. 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. There is also an effort at comparison of various techniques and lingering of the IV-2SLS; but I feel either the comparison should have flowed through the entire book, or should have been chapterized separately. In places where the story of a DD is flowing, an IV comparison takes one off guard in terms of now being able to apply and compare.

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. The Global Crisis provoked some to ask, “what’s the use of economics”?, a reference to the economics that most economists had studied in college. We’d pile on, adding, “what’s the use of econometrics… at least as currently taught”? Most of the undergraduates who major in economics take a course in econometrics. This course should be one of the more useful experiences a student can have. For decades, economics undergraduates have found jobs in sectors that make heavy use of quantitative skills. As data sets have grown bigger and more complex, the demand for new grads with data-analytic skills has accelerated rapidly. Econometrics courses promise to equip our students with the powerful tools economists use to understand the economic relationships hidden in data. It’s both remarkable and regrettable, therefore, that econometrics classes continue to transmit an abstract body of knowledge that’s largely irrelevant for economic policy analysis, business problems, and even for much of the econometric research undertaken by scholars. The unapologetic focus on causal relationships that’s emblematic of modern applied econometrics emerged gradually in the 1980s and has since accelerated. 1 Today’s econometric applications make heavy use of quasi-experimental research designs and randomised trials of the sort once seen only in medical research. In fact, the notion of a randomised experiment has become a fundamental unifying concept for most applied econometric research. Even where random assignment is impractical, the notion of the experiment we’d like to run guides our choice of empirical questions and disciplines our use of non-experimental tools and data. Admitting that the academic way keeps the writing clean, but then it also makes the reader lose interest. The snippets are like the buzz generators - they are the interest makers - and this book could have gone a long long way in making 'Metrics fun!.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 Wooldridge, JM (2012), Introductory Econometrics: A Modern Approach, South-Western Cengage Learning. Endnotes

The Regression Discontinuity Designs are depicted in chapter 4 and distinguished from the instrumental variables approach. The fact that variables in here have a fixed cutoff point - resulting from an external rule - which either completely determines how a treatment manifests or increases its likelihood, is illustrated. Individuals close to this cut-off can be seen as equal in other characteristics. For example, Angrist and Pischke investigate whether young adults die more often on their 21st birthday. The regression discontinuity in the mortality rate around the birthday is then interpreted as an indicator for the effect of the minimum legal drinking age, defined by law ("Some young people appear to pay the ultimate price for the privilege of downing a legal drink", p. 164). The basic idea why this method is also a robust path to causal inference is explicitly discussed. Personally I found the extended metaphor that econometrics is kung fu to be annoying. I think the authors believed that they were making the material more accessible by treating it less reverently, which I agree could have been an effective communication strategy, but I think it mostly fell flat. If I'm cringing at your puns I'm not learning about local average treatment effects. Moreover, I think the metaphor that econometrics is kung fu is actually harmful. Kung fu is mysterious and mystical. It's studied at the feet of a master over the course of a lifetime. The master might have you wash floors for a year, without offering a reason. 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. Others may disagree but this grasshopper would have preferred we spend less time in the dojo and more time in the computer lab. The chapters I feel are also imbalanced. Take for instance - Chapters on Regression, RDD are flowing smoothly, but the chapter on IV is tighter than the others. On the merit of how much does the book intend to give the reader the details on these things is another issue. But given a cursory exposition on this, I think IV overdoes it, whereas other chapters are more pointed and do not bring out unnecessary details.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 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. I would be hard pressed to name another econometrics book that can be read for enjoyment yet provides useful quantitative insights."— Financial Analysts Journal Economists view data scientists as regression monkeys (probably the worst insult you can give someone in economics). When they look at data science they just see extremely elaborate efforts at curve fitting. Since economists don't think curve fitting is all that interesting or useful for doing economics, they scoff at neural networks and boosting. Imagine their horror when they see data science moving into their territory.

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. So 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. 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 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.As already introduced in the first chapter, treatment and control groups are not necessarily equal in all other aspects, especially under non-randomized conditions. Therefore, the idea of "Regression" is discussed in the next chapter. Regression is presented as a conditioning technique that only delivers credible results if all variables that introduce group differences apart from the treatment are observed. Such variables are then computationally made equal across the groups, so that causal inference can be made. The authors emphasize that, in most natural settings, selection bias can have multiple sources that are usually not all observable. In such cases, the power of regression is limited. Angrist, JD, and J-S Pischke (2015), Mastering Metrics: The Path from Cause to Effect, Princeton University Press. 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. 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.