Mastering 'Metrics: The Path from Cause to Effect

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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

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 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. 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. 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 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.

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.

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 See, for example, Table 4 in Hamermesh (2013), which highlights the increasing analysis of user-generated data, much coming from experiments and quasi-experimental research designs. 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.

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 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 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 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.

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. 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 UniversityAnother relevant factor with the book is that the passages do not lead you to read on - rather they are too academic! If the intention is for a wider audience and for a more diversified crowd, then the importance of leading readers onto the next issues is of supreme importance. For eg: they are discussing an issue and then the next issue comes up as a next section. There is no sense of direction as to why am I reading about an issue and where do the connections matter - in terms of comprehending the entire topic, the reader is left on his own. In terms of the chapters itself, I think they are very topical and will cover a lot of the modern research; the book pulls away from a fundamental issue - no matter what the methods are, the thought of comparison and counterfactuals is not emphasized enough I feel. Consider a standard econometrics textbook - say Wooldridge - it actually draws a framework where you know - no matter what the empirical problem is, you need to think in terms of identification, endogeneity and the underlying logic of counter-factuals. They certainly bring in a lot of that - where they talk about apples-to-apples comparison; but the emphasis is not approached as a general method of empirical analysis and the book can go far if that is emphasized. Thus in terms of binding the various methods - (a) a comparison and (b) a generalized empirical strategy might help get the econometrics logic through to a wider audience.

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. 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 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. 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 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.