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 topicsCausal 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.

《社会科学因果推断的理论基础》系统介绍了反事实的因果推论框架以及如何采用倾向值方法帮助社会科学经验研究者进行因果推论。除了基本的统计学原理之外，《社会科学因果推断的理论基础》回顾了倾向值方法的历史、发展及其对调查研究的意义，以及如何利用倾向值方法处理因果关系中的多类别性、中介性与异质性。除此之外，《社会科学因果推断的理论基础》还通过专门章节分析了比较个案研究中的综合控制个案方法以及因果推论过程中如何确定分析样本的样本量以及统计检定力。