Mastering 'Metrics is an engaging, fun, and highly accessible guide to the paradigm of causal inference.

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. If someone makes a decision between two competing choices, selection bias can intervene - for example, an average person going to a hospital is actually unhealthier than someone who doesn’t go, but that is because we are looking at two different types of people!

In the fourth section, the authors cover how breaks and discontinuities offer opportunities for uncovering useful information. We explain why these questions are challenging and why simple empirical strategies to address them can be misleading.

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. Becker, WE, and WH Greene (2001), “Teaching Statistics and Econometrics to Undergraduates”, Journal of Economic Perspectives, 169-182. Core economic questions are mentioned in passing if at all, and empirical examples are still mostly contrived, as in Studenmund (2011), who introduces empirical regression with a fanciful analysis of the relationship between height and weight. The first empirical application in Hill, Griffiths, and Lim (2011: 49) explores the correlation between food expenditure and income. I did not expect the details of an econometrics course; but I thought as the book started out, it would have a funny take and make things interesting for the reader.With an engaging, insightful style, Angrist and Pischke catch readers up on five powerful methods in this area. Calendar arrived safely in plastic-free packaging - which I've not seen before with a calendar, so kudos for that. It gives you circumstances where economists thrive with their data work and where they build fascinating analysis from interesting natural accidents. I really like how Angrist and Pischke break down and discuss the topics covered (randomized trials, regression, instrumental variables, regression discontinuity designs, differences-in-differences). I do hope that most of my time in my data career I can jump straight to the gold standard of randomized controlled tests, but sometimes that's not feasible, and this book offers a wealth of alternative solutions.

There's lots of math to be found in the book, but the authors gently walk the reader through each equation; I'd say little more than a basic knowledge of algebra is required (although some fluency with probability wouldn't hurt). 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.This showed that people receiving more healthcare were not significantly more healthy - which seemed a disappointing result. Just like healthcare, Steve suggests there is little indication that the more you spend on education the greater the eventual benefit - however that benefit may be measured. Its value turns solely on the core notion that controlled comparisons are more likely than uncontrolled comparisons to have a causal interpretation. Posing several well-chosen empirical questions in social science, Mastering 'Metrics develops methods to provide the answers and applies them to interesting datasets.

While it adequately covers core econometric concepts, I'm not convinced it effectively balances a textbook approach vs that of a "how-to" guide: it likely would have been better suited leaning a bit more in one direction or another.

Jörn-Steffen Pischke is professor of economics at the London School of Economics and Political Science. I started reading this book as a 'forced formula' in a standard Labor Economics course but never ended up reading the whole thing, until I decided to pick it up again.