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Product details

File Size: 9723 KB

Print Length: 644 pages

Simultaneous Device Usage: Up to 4 simultaneous devices, per publisher limits

Publisher: Cambridge University Press; 1 edition (January 31, 2015)

Publication Date: March 24, 2015

Sold by: Amazon Digital Services LLC

Language: English

ASIN: B00Q8TWRGC

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Some people are of the opinion that statistics should replace calculus in high school/college curricula because of the importance of analytics in the modern business world. I think a similar argument could be made about replacing some of the more esoteric parts of graduate statistics/econometrics with causal inference theory. If that opinion is correct, this would be an excellent reference text.Causal inference theory is important because the regression techniques now taught to young social scientists as methods of determining cause and effect assume endogeneity when the data often don't support such an assumption. They also impose a linear model on the data that can be similarly inappropriate. The non-parametric techniques discussed by Rubin and Imbens, while having their own assumptions, are applicable to a wider range of problems.Rubin and Imbens summarize the voluminous literature on propensity score and related causal inference techniques in a manner that is accessible to someone with a solid background in statistics (both frequentist and Bayesian). I read the book cover to cover and, despite already knowing something about Propensity Score techniques, learned a great deal.They begin with randomized experiments then explain how the mathematical models developed for such methods are also applicable to observational studies. They then discuss various methods of using the Propensity Score along with tests of the plausibility of such models and bias limits when some of the assumptions in these models are relaxed.One complaint I have is that the different types of exact matching are barely discussed. Considering the growing importance of techniques like Coarsened Exact Matching, this seems like a significant oversight. In addition, the book contains no exercises making it difficult to use as a textbook without some supplementary material.All in all, though, this work is a must have for those engaged in Causal Inference either academically or in the business world. Even those not making active use of these techniques might find applications to their empirical work once they understand how to properly use Propensity Score analysis.

This book does not have a very rigorous approach to causation. The applied parts are lacking as well -- they apply the suggested models in “empirical examples” but that's literally it - they just run the models and read the numbers, no insightful interpretation of anything, no justification and so on. If you want to read something informal but applied, go to Mostly Harmless Econometrics by Angrist and Pischke. If you want to read something rigorous, go to Causality by Judea Pearl.

I really love it.

The only shortcoming is that font is small. If the margin is reduced to give more space to the context, that would be perfect!

A great book. It has careful and easy (to follow) arguments that help to understand interestign situations.

Although this might be a good book to study causal inference since there are not many choices for now, it was tediously long in many topics. I wish it were written in a more concise way.

Recommended strongly by Prof. Strauss, really the best!

Imbens & Rubin are masters.

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