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><I>Causal Inference: What If</I></i></b> 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 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<br>? Includes software and real data examples, as well as ?Fine Points? and ?Technical Points? throughout to elaborate on certain key topics</p><p><b><i><I>Causal Inference: What If</I></i></b> 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.<p></p></p>