## Observation and experimentrak ecu

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 dataAs 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 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 establishedI 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 thisI 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 Paul Rosenbaum 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 statisticsThankfully 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 techniues in their daily workI 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 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 essentialsInevitably 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 outEven 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 techniues Paul Rosenbaum 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 schoolI recommend this one to anyone who is interested about causal inference Paul Rosenbaum Brings reality home Not anecdotal stuff Paul Rosenbaum I liked it but I should admit that I haven t read a verbose book on stats Paul Rosenbaum A daily glass of wine prolongs life yet alcohol can cause life threatening cancer Some say raising the minimum wage will decrease ineuality while others say it increases unemployment Scientists once confidently claimed that hormone replacement therapy reduced the risk of heart disease but now they eually confidently claim it raises that risk What should we make of this endless barrage of conflicting claims Observation and Experiment is an introduction to causal inference by one of the field s leading scholars An award winning professor at Wharton Paul Rosenbaum explains key concepts and methods through lively examples that make abstract principles accessible He draws his examples from clinical medicine economics public health epidemiology clinical psychology and psychiatry to explain how randomized control trials are conceived and designed how they differ from observational studies and what techniues are available to mitigate their bias Carefully and precisely writtenreflecting superb statistical understanding all communicated with the skill of a master teacher Stephen M Stigler author of The Seven Pillars of Statistical Wisdom An excellent introductionWell written and thoughtfulfrom one of causal inference s noted experts Journal of the American Statistical Association Rosenbaum is a gifted expositoran outstanding introduction to the topic for anyone who is interested in understanding the basic ideas and approaches to causal inference Psychometrika A very valuable contributionHighly recommended International Statistical ReviewA daily glass of wine prolongs life yet alcohol can cause life threatening cancer Some say raising the minimum wage will decrease inequality while others say it increases unemployment Scientists once confidently claimed that hormone replacement therapy reduced the risk of heart disease but now they equally confidently claim it raises that risk What should we make of this endless barrage of conflicting claims Observation and Experiment is an introduction to causal inference by one of the field s leading scholars An award winning professor at Wharton.

#heading[2]Author of The Seven Pillars of Statistical Wisdom An excellent introduction Well written and thoughtful from one of causal inference s noted experts Journal of the American Statistical Association Rosenbaum is a gifted expositor an outstanding introduction to the topic for anyone who is interested in understanding the basic ideas and approaches to causal inference Psychometrika A very valuable contribution Highly recommended International Statistical ReviewA daily glass of wine prolongs life yet alcohol can cause life threatening cancer Some say raising the minimum wage will decrease inequality while others say it increases unemployment Scientists once confidently claimed that hormone replacement therapy reduced the risk of heart disease but now they equally confidently claim it raises that risk What should we make of this endless barrage of conflicting claims Observation and Experiment is an introduction to causal inference by one of the field s leading scholars An award winning professor at Wharton.

#heading[3]Paul Rosenbaum explains key concepts and methods through lively examples that make abstract principles accessible He draws his examples from clinical medicine economics public health epidemiology clinical psychology and psychiatry to explain how randomized control trials are conceived and designed how they differ from observational studies and what techniques are available to mitigate their bias Carefully and precisely written reflecting superb statistical understanding all communicated with the skill of a master teacher Stephen M Stigler Paul Rosenbaum explains key concepts and methods through lively examples that make abstract principles accessible He draws his examples from clinical medicine economics public health epidemiology clinical psychology and psychiatry to explain how randomized control trials are conceived and designed how they differ from observational studies and what techniques are available to mitigate their bias Carefully and precisely written reflecting superb statistical understanding all communicated with the skill of a master teacher Stephen M Stigler author of The Seven Pillars of Statistical Wisdom An excellent introduction Well written and thoughtful from one of causal inference s noted experts Journal of the American Statistical Association Rosenbaum is a gifted expositor an outstanding introduction to the topic for anyone who is interested in understanding the basic ideas and approaches to causal inference Psychometrika A very valuable contribution Highly recommended International Statistical Review Observation and Experiment An Introduction to Causal Inference .