The application of causal inference methods is growing exponentially in fields that deal with observational data. Written by pioneers in the field, this practic
One of the primary motivations for clinical trials and observational studies of humans is to infer cause and effect. Disentangling causation from confounding is
An accessible, contemporary introduction to the methods for determining cause and effect in the social sciences "Causation versus correlation has been the basis
A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning. The mathematization of causality is
The book begins with a comprehensive introduction to mediation analysis, including chapters on concepts for mediation, regression-based methods, sensitivity ana