In this second edition of Counterfactuals and Causal Inference‎,‎ completely revised and expanded‎,‎ the essential features of the counterfactual approach to observational data analysis are presented with examples from the social‎,‎ demographic‎,‎ and health sciences‎.‎ Alternative estimation techniques are first introduced using both the potential outcome model and causal graphs‎;‎ after which‎,‎ conditioning techniques‎,‎ such as matching and regression‎,‎ are presented from a potential outcomes perspective‎.‎ For research scenarios in which important determinants of causal exposure are unobserved‎,‎ alternative techniques‎,‎ such as instrumental variable estimators‎,‎ longitudinal methods‎,‎ and estimation via causal mechanisms‎,‎ are then presented‎.‎ The importance of causal effect heterogeneity is stressed throughout the book‎,‎ and the need for deep causal explanation via mechanisms is discussed‎.‎