Causal Inference Av Miguel A. (harvard School Of Public Health Boston Massachusetts Usa) Hernan, James M. (harvard School Of Pub Health Boston Massach

<P>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, <B><I>Causal Inference: What If</B></I> 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.</P><P><B>FEATURES:</B><BR>¿ Emphasizes taking the causal question seriously enough to articulate it with sufficient precision <BR>¿ 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<BR>¿ Describes causal diagrams, both directed acyclic graphs and single-world intervention graphs <BR>¿ Explains variou

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