Free Download Observation and Experiment: An Introduction to Causal Inference
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Observation and Experiment: An Introduction to Causal Inference
Free Download Observation and Experiment: An Introduction to Causal Inference
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Review
"Observation and Experiment, by Paul Rosenbaum, lives up to its subtitle: it provides an excellent Introduction to Causal Inference. ... The book is a well-written and thoughtful reflection on the doing of causal inference from one of causal inference's noted experts." Journal of the American Statistical Association, 2018, Volume 113, pp. 1392-1393."Rosenbaum is a gifted expositor, and as a result, this book is an outstanding introduction to the topic for anyone who is interested in understanding the basic ideas and approaches to causal inference. ... In Observation and Experiment, Paul Rosenbaum has found just the right balance, providing readers with a precise, accessible, and enjoyable entrée to the field of causal inference." Psychometrika, 13 August 2018 doi.org/10.1007/s11336-018-9632-y"The author's voice is an important element in the book's success. Rosenbaum is consistently clear and direct, and seems at times to be speaking directly to the reader. His excellent set of examples (twenty-five of them altogether) bring the more theoretical discussions to life."Mathematical Association of America 14 August 2017 maa.org/press/maa-reviews/observation-and-experiment-an-introduction-to-causal-inference"Most of the main text of the book is written without formulas or how to implement them in R [...but...] these technical details are ... given in 59 pages of footnotes at the end of the book, which also contain references for further study ... The book is a very valuable contribution ... highly recommended."International Statistical Review 2018, volume 86, 165-166."A researcher seeking instruction in the sophisticated use of such techniques may want to consult Observation and Experiment: An Introduction to Causal Inference, by the statistician Paul R. Rosenbaum." New York Times Book Review, 16 February 2018 nytimes.com/2018/02/16/books/review/science-inference-data.html
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From the Author
Find more information about Observation and Experiment on my webpage,www-stat.wharton.upenn.edu/~rosenbap/
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Product details
Hardcover: 400 pages
Publisher: Harvard University Press (August 14, 2017)
Language: English
ISBN-10: 067497557X
ISBN-13: 978-0674975576
Product Dimensions:
6.4 x 1.4 x 9.3 inches
Shipping Weight: 1.6 pounds (View shipping rates and policies)
Average Customer Review:
4.2 out of 5 stars
8 customer reviews
Amazon Best Sellers Rank:
#702,213 in Books (See Top 100 in Books)
Albert Einstein once said, "All physical theories, their mathematic expression apart, ought to lend themselves to so simple a description that even a child could understand them." Having myself taught graduate students and published papers on causal inference, I know the difficulty of rising to Einstein's standard when teaching this branch of statistics.Thankfully, Paul Rosenbaum has accomplished this task in Observation and Experiment. Himself one of the leaders in the causal inference revolution, publishing with Rubin the classic 1983 Biometrika article on propensity score analysis, Rubinstein describes the essentials of proper causal inference work in terms that should be comprehensible for the wide variety of social scientists, analytics professionals and other researchers who use causal inference techniques in their daily work.I can think of several potential audiences for this text. First, undergraduates majoring in statistics or related disciplines would benefit from the intuitive explanations of causal inference theory provided here before graduating to more mathematically sophisticated accounts. Second, researchers in the private or public sectors who want to improve the accuracy of their casual inference analyses by a deeper understanding of such concepts as sensitivity analysis or elaborate theories will find the explanations in this text at their level of statistical sophistication. Lastly, professors who are looking for ways to better teach causal inference will find many useful examples and descriptions that will make it easier for their students to grasp the essentials.Inevitably, in a text like this, there are parts of causal inference theory that are left out. All the work done by Gary King and others on Coarsened Exact Matching is not even referenced. The complementary but different approach to causal inference developed by Judea Pearl is similarly left out.Even so, to be able to write in a conversational and informative style about causal inference is a significant intellectual achievement. I doubt I will teach the material in Observation and Experiment to my seven year old, as Einstein's criteria might imply, but this book is at the right level for undergraduates and laymen to deepen and strengthen their understanding of these increasingly used statistical techniques.
This is an excellent introduction and explanation of how to use statistics to reason. I learned a lot about how one should design experiments so that they you can know how strong of a conclusion you can make from the data.As a person who doesn't mind the math, I was a little disappointed that the math isn't included, but Rosenbaum always has a reference to look up for more details, so that my disappointment was mollified. The advantage of this is getting to read Rosenbaum's lucid explanations in "plain English". He does an excellent job of explaining things with good examples, and making sure that the limits of the examples are established.I was somewhat familiar with many of the concepts, but Rosenbaum does a great job of explaining the though processes for randomized experiments, natural experiments, observational studies, how to produce matches for treatment vs control, and (most interesting for me) how to look at the sensitivity of these studies to bias. Design sensitivity is extremely well explained and something extremely important. Essentially knowing the robustness of the evidence is always difficult and sensitivity analysis lets you get at this.I would recommend this to anyone who has an interest in statistical methods. It is very accessible and Rosenbaum is a very good writer. Whether or not you like math, the book is very good at explaining its concepts and elucidating them with examples. Its examples are heavily tilted toward the medical field, but they are not exclusive to it by any means. Overall, just a very good introduction and explanation of concepts.
I really love the "accessible precision" approach provided by this book. This is the best material on causal inference that I have ever read. I wish I could have this book earlier when I was in grad school.I recommend this one to anyone who is interested about causal inference.
I liked it but I should admit that I haven’t read a more verbose book on stats.
This book was born outdated, both in causal and statistical terms. For starters, it doesn't cover most of causal inference research. Also, it uses acritically null hypothesis significance testing all around. This book might be classified as history book, where you learn how old "science" used to be done, where researchers just controlled for everything they could, do null hypothesis significance testing, and some ad-hoc sensitivity analysis... The examples are entertaining though, that's why I'm giving 2 stars.
I have to point out that there are people who are critical of Randomized Control Experiments (RCEs) Like Professors Ziliak and McCloskey, critics who get a passing mention here in the beginning but not by name. So when the RCEs are held up as the gold standard of understanding causation in this book, I'm a little circumspect. Overall though, it is a very accessible book about how we can pull apart causation using the tools most widely used in the academy.
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