Causal Inference for Statistics, Social, and Biomedical Sciences
by Guido W. Imbens, Donald B. Rubin · first published 2015
67% recommend33% neutral
Most questions in social and biomedical sciences are causal in nature: what would happen to individuals, or to groups, if part of their environment were changed? In this groundbreaking text, two world-renowned experts present statistical methods for studying such questions. This book starts with the notion of potential outcomes, each corresponding to the outcome that would be realized if a subject were exposed to a particular treatment or regime. In this approach, causal effects are comparisons of such potential outcomes. The fundamental problem of causal inference is that we can only observe
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In econometrics / empirical social science: - Mostly Harmless Econometrics by Joshua Angrist and Jorn Steffan Piscke - Causal Inference for Statistics, Social, and Biomedical Sciences by Guido Imbens and Donald Rubin - Data Analysis using Regression and Multilevel Models by Andrew Gelman and Jennifer Hill
on “Ask HN: What are the best textbooks in your field of expertise?” ↗Imbens and Rubin’s book “Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction” is an excellent reference for the Potential Outcomes approach. There are also several summaries online done by Rubin which do a great job of explaining the core concepts and how they…
on “The Case for Causal AI” ↗…in the CS side of things tend to use Pearl's DAGS for conceptualizing this stuff. I'm in the stats/econ side of things so I use Neyman-Rubin. They're equivalent. Allow me to suggest Rubin and Imbens - Causal Inference for Statistics, Social and Biomedical Sciences as a good textbook that we assign to graduate students learning this stuff. Some of my students tell me the "Causal Inference Mixtape" is popular among people who want less statistical theory and more…
on “Popularity of technology on Stack Overflow and Hacker News: Causality Analysis” ↗…a clear and convincing job putting causality (esp. w/ data fusion!) at the heart of modern systems (including ML). For those on a more mathematical bend, check out "Causality" by the same author or "Causal Inference for Statistics, Social, and Biomedical Sciences" by Imbens and Rubin
on “Sometimes, requiring explanability is counterproductive” ↗I was looking for a review, and found Gelman's recommendation of Causal Inference for Statistics, Social, and Biomedical Sciences and mention of this book: http://www.hsph.harvard.edu/miguel-hernan/causal-inference-b... Does anyone have a comment on those? I've read Pearl's two earlier books, and found the one on causality quite…
on “Causal Inference in Statistics: A Primer” ↗…two pretty different perspectives. But for this to work, you need a lot of data: both lots of units (e.g. people), and a lot of information about each individual unit. [0] Causality, Judea Pearl [1] Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction, Guido Imbens and Donald Rubin
on “Simpson’s Paradox (2016)” ↗