Causal Inference: What If. Hernan, M. A. & Robins, J. M. CRC Press, Boca Raton, December, 2025. abstract bibtex Causal inference is a complex scientific task that relies on evidence from multiple sources and a variety of methodological approaches. By providing a cohesive presentation of concepts and methods that are currently scattered across journals in several disciplines, Causal Inference: What If provides an introduction to causal inference for scientists who design studies and analyze data. The book is divided into three parts of increasing difficulty: causal inference without models, causal inference with models, and causal inference from complex longitudinal data. FEATURES: • Emphasizes taking the causal question seriously enough to articulate it with sufficient precision • Shows that causal inference from observational data relies on subject-matter knowledge and therefore cannot be reduced to a collection of recipes for data analysis • Describes causal diagrams, both directed acyclic graphs and single-world intervention graphs • Explains various data analysis approaches to estimate causal effects from individual-level data, including the g-formula, inverse probability weighting, g-estimation, instrumental variable estimation, outcome regression, and propensity score adjustment • Includes software and real data examples, as well as ‘Fine Points’ and ‘Technical Points’ throughout to elaborate on certain key topics Causal Inference: What If has been written for all scientists that make causal inferences, including epidemiologists, statisticians, psychologists, economists, sociologists, political scientists, computer scientists, and more. The book is substantially class-tested, as it has been used in dozens of universities to teach courses on causal inference at graduate and advanced undergraduate level.
@book{hernan_causal_2025,
address = {Boca Raton},
title = {Causal {Inference}: {What} {If}},
isbn = {978-1-315-37493-2},
shorttitle = {Causal {Inference}},
abstract = {Causal inference is a complex scientific task that relies on evidence from multiple sources and a variety of
methodological approaches. By providing a cohesive presentation of concepts and methods that are currently
scattered across journals in several disciplines, Causal Inference: What If provides an introduction to causal
inference for scientists who design studies and analyze data. The book is divided into three parts of increasing
difficulty: causal inference without models, causal inference with models, and causal inference from complex
longitudinal data.
FEATURES:
• Emphasizes taking the causal question seriously enough to articulate it with sufficient precision
• Shows that causal inference from observational data relies on subject-matter knowledge and therefore
cannot be reduced to a collection of recipes for data analysis
• Describes causal diagrams, both directed acyclic graphs and single-world intervention graphs
• Explains various data analysis approaches to estimate causal effects from individual-level data, including
the g-formula, inverse probability weighting, g-estimation, instrumental variable estimation, outcome
regression, and propensity score adjustment
• Includes software and real data examples, as well as ‘Fine Points’ and ‘Technical Points’ throughout to
elaborate on certain key topics
Causal Inference: What If has been written for all scientists that make causal inferences, including epidemiologists,
statisticians, psychologists, economists, sociologists, political scientists, computer scientists, and more. The book is substantially class-tested,
as it has been used in dozens of universities to teach courses on causal inference at graduate and advanced undergraduate level.},
publisher = {CRC Press},
author = {Hernan, Miguel A. and Robins, James M.},
month = dec,
year = {2025},
}
Downloads: 0
{"_id":"Sy7v9agpYDJyavLBT","bibbaseid":"hernan-robins-causalinferencewhatif-2025","author_short":["Hernan, M. A.","Robins, J. M."],"bibdata":{"bibtype":"book","type":"book","address":"Boca Raton","title":"Causal Inference: What If","isbn":"978-1-315-37493-2","shorttitle":"Causal Inference","abstract":"Causal inference is a complex scientific task that relies on evidence from multiple sources and a variety of methodological approaches. By providing a cohesive presentation of concepts and methods that are currently scattered across journals in several disciplines, Causal Inference: What If provides an introduction to causal inference for scientists who design studies and analyze data. The book is divided into three parts of increasing difficulty: causal inference without models, causal inference with models, and causal inference from complex longitudinal data. FEATURES: • Emphasizes taking the causal question seriously enough to articulate it with sufficient precision • Shows that causal inference from observational data relies on subject-matter knowledge and therefore cannot be reduced to a collection of recipes for data analysis • Describes causal diagrams, both directed acyclic graphs and single-world intervention graphs • Explains various data analysis approaches to estimate causal effects from individual-level data, including the g-formula, inverse probability weighting, g-estimation, instrumental variable estimation, outcome regression, and propensity score adjustment • Includes software and real data examples, as well as ‘Fine Points’ and ‘Technical Points’ throughout to elaborate on certain key topics Causal Inference: What If has been written for all scientists that make causal inferences, including epidemiologists, statisticians, psychologists, economists, sociologists, political scientists, computer scientists, and more. The book is substantially class-tested, as it has been used in dozens of universities to teach courses on causal inference at graduate and advanced undergraduate level.","publisher":"CRC Press","author":[{"propositions":[],"lastnames":["Hernan"],"firstnames":["Miguel","A."],"suffixes":[]},{"propositions":[],"lastnames":["Robins"],"firstnames":["James","M."],"suffixes":[]}],"month":"December","year":"2025","bibtex":"@book{hernan_causal_2025,\n\taddress = {Boca Raton},\n\ttitle = {Causal {Inference}: {What} {If}},\n\tisbn = {978-1-315-37493-2},\n\tshorttitle = {Causal {Inference}},\n\tabstract = {Causal inference is a complex scientific task that relies on evidence from multiple sources and a variety of \nmethodological approaches. By providing a cohesive presentation of concepts and methods that are currently \nscattered across journals in several disciplines, Causal Inference: What If provides an introduction to causal \ninference for scientists who design studies and analyze data. The book is divided into three parts of increasing \ndifficulty: causal inference without models, causal inference with models, and causal inference from complex \nlongitudinal data.\nFEATURES:\n• Emphasizes taking the causal question seriously enough to articulate it with sufficient precision \n• Shows that causal inference from observational data relies on subject-matter knowledge and therefore \ncannot be reduced to a collection of recipes for data analysis\n• Describes causal diagrams, both directed acyclic graphs and single-world intervention graphs \n• Explains various data analysis approaches to estimate causal effects from individual-level data, including \nthe g-formula, inverse probability weighting, g-estimation, instrumental variable estimation, outcome \nregression, and propensity score adjustment\n• Includes software and real data examples, as well as ‘Fine Points’ and ‘Technical Points’ throughout to \nelaborate on certain key topics\nCausal Inference: What If has been written for all scientists that make causal inferences, including epidemiologists, \nstatisticians, psychologists, economists, sociologists, political scientists, computer scientists, and more. The book is substantially class-tested, \nas it has been used in dozens of universities to teach courses on causal inference at graduate and advanced undergraduate level.},\n\tpublisher = {CRC Press},\n\tauthor = {Hernan, Miguel A. and Robins, James M.},\n\tmonth = dec,\n\tyear = {2025},\n}\n\n\n\n\n\n\n\n","author_short":["Hernan, M. A.","Robins, J. M."],"key":"hernan_causal_2025","id":"hernan_causal_2025","bibbaseid":"hernan-robins-causalinferencewhatif-2025","role":"author","urls":{},"metadata":{"authorlinks":{}},"html":""},"bibtype":"book","biburl":"https://bibbase.org/zotero/chiraaggohel","dataSources":["akApyGuSiBYkDccny"],"keywords":[],"search_terms":["causal","inference","hernan","robins"],"title":"Causal Inference: What If","year":2025}