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.
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.
Produkteigenschaften
- Artikelnummer: 9781420076165
- Medium: Buch
- ISBN: 978-1-4200-7616-5
- Verlag: Taylor & Francis Inc
- Erscheinungstermin: 30.07.2025
- Sprache(n): Englisch
- Auflage: 1. Auflage 2025
- Produktform: Gebunden
- Gewicht: 453 g
- Seiten: 312
- Format (B x H): 210 x 280 mm
- Ausgabetyp: Kein, Unbekannt