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.

The past few decades have seen an explosion of research on causal reasoning in philosophy, computer science, and statistics, as well as descriptive work in psychology. In Causation with a Human Face, James Woodward integrates these lines of research and argues for an understanding of how each can inform the other: normative ideas can suggest interesting experiments, while descriptive results can suggest important normative concepts. Woodward's overall framework builds on the interventionist treatment of causation that he developed in Making Things Happen. Normative ideas discussed include proposals about the role of invariant or stable relationships in successful causal reasoning and the notion of proportionality. He argues that these normative ideas are reflected in the causal judgments that people actually make as a descriptive matter.
Woodward also discusses the common philosophical practice-particularly salient in philosophical accounts of causation—of appealing to "intuitions" or "judgments about cases" in support of philosophical theses. He explores how, properly understood, such appeals are not different in principle from appeals to results from empirical research, and demonstrates how they may serve as a useful source of information about causal cognition.