Imbens and rubin causal inference book
Causal Inference for Statistics, Social, and Biomedical Sciences
One of their examples is giving me trouble : I provide the code to replicate what I did at the end of the post, after a short description of the problem for those not familiar with the methodology in the book. Not surprisingly, the book advocates the use of the Rubin Causal Model RCM that uses the potential outcomes framework. We of course only observe one of these two potential outcomes for each individual depending on her treatment assignment. The crux of this framework is that it clearly turns the problem of causal inference into a missing data problem. In fact, most causal inference methods can be mapped into different ways to impute the missing outcomes.
Guido W. Donald B. Rubin is John L. Loeb Professor of Statistics at Harvard University, where he has been professor since and department chair for thirteen of those years. He has authored or coauthored nearly four hundred publications including ten books , has four joint patents, and has made important contributions to statistical theory and methodology, particularly in causal inference, design and analysis of experiments and sample surveys, treatment of missing data, and Bayesian data analysis. Rubin has received the Samuel S. He is one of the most highly cited authors in mathematics and economics with nearly , citations to date.
coloradoprimetax.com: Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction (): Guido W. Imbens, Donald B. Rubin: Books.
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Guido Imbens and Don Rubin recently came out with a book on causal inference. Imbens and Rubin come from social science and econometrics., Imbens and Donald B. Rubin in Cambridge Books from Cambridge University Press Abstract: 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?