What constitutes a causal explanation, and must an explanation be causal? What warrants a causal inference, as opposed to a descriptive regularity? What techniques are available to detect when causal
What constitutes a causal explanation, and must an explanation be causal? What warrants a causal inference, as opposed to a descriptive regularity? What techniques are available to detect when causal
The importance of educational certification for labor market success has increased since the 1970s. But social sciences still cannot answer a fundamental question: Who goes to college and why? In On
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.
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.
Did mandatory busing programs in the 1970s increase the school achievement of disadvantaged minority youth? Does obtaining a college degree increase an individual's labor market earnings? Did the use
In this book, the essential features of the counterfactual model of causality for observational data analysis are presented with examples from sociology, political science, and economics. The importan
How often do working-class children obtain college degrees and then pursue professional careers? Conversely, how frequently do the children of doctors and lawyers fail to enter high status careers up
Morgan (sociology, Cornell U.), Grusky (sociology, Stanford U.), and Fields' (labor economics, Cornell U.) text originated as a series of presentations at a March 2003 conference held in Ithaca, New Y
After 30 years of economic reform, China has reached a crossroads in its development process, and faces many challenges in the use of natural resources, the living environment, and the economic, socia
