Publisher's Synopsis
Provides a cohesive presentation of concepts and methods for causal inference that are currently scattered across journals in several disciplinesEmphasizes the need to take the causal question seriously enough to articulate it with sufficient precisionShows that causal inference from observational data cannot be reduced to a collection of recipes for data analysis, as subject-matter knowledge is required to justify the necessary assumptionsDescribes causal diagrams, both directed acyclic graphs and single-world intervention graphs, to represent causal inference problemsDescribes various data analysis approaches to estimate the causal effect of interest, including the g-formula, inverse probability weighting, g-estimation, instrumental variable estimation, and propensity score adjustmentIncludes 'Fine Points' and 'Technical Points' throughout to elaborate on certain key topics, as well as software and real data examples.
