Christopher Winship – författare

Pragmatist thought is central to sociology. However, sociologists typically encounter pragmatism indirectly, as a philosophy of science or as an influence on canonical social scientists, rather than as a vital source of theory, research questions, and methodological reflection in sociology today.In The New Pragmatist Sociology, Neil Gross, Isaac Ariail Reed, and Christopher Winship assemble a range of sociologists to address essential ideas in the field and their historical and theoretical connection to classical pragmatism. The book examines questions of methodology, social interaction, and politics across the broad themes of inquiry, agency, and democracy. Essays engage widely and deeply with topics that motivate both pragmatist philosophy and sociology, including rationality, speech, truth, expertise, and methodological pluralism.Contributors include Natalie Aviles, Karida Brown, Daniel Cefaï, Mazen Elfakhani, Luis Flores, Daniel Huebner, Cayce C. Hughes, Paul Lichterman, John Levi Martin, Ann Mische, Vontrese D. Pamphile, Jeffrey N. Parker, Susan Sibley, Daniel Silver, Mario Small, Iddo Tavory, Stefan Timmermans, Luna White, and Joshua Whitford.

Pragmatist thought is central to sociology. However, sociologists typically encounter pragmatism indirectly, as a philosophy of science or as an influence on canonical social scientists, rather than as a vital source of theory, research questions, and methodological reflection in sociology today.In The New Pragmatist Sociology, Neil Gross, Isaac Ariail Reed, and Christopher Winship assemble a range of sociologists to address essential ideas in the field and their historical and theoretical connection to classical pragmatism. The book examines questions of methodology, social interaction, and politics across the broad themes of inquiry, agency, and democracy. Essays engage widely and deeply with topics that motivate both pragmatist philosophy and sociology, including rationality, speech, truth, expertise, and methodological pluralism.Contributors include Natalie Aviles, Karida Brown, Daniel Cefaï, Mazen Elfakhani, Luis Flores, Daniel Huebner, Cayce C. Hughes, Paul Lichterman, John Levi Martin, Ann Mische, Vontrese D. Pamphile, Jeffrey N. Parker, Susan Sibley, Daniel Silver, Mario Small, Iddo Tavory, Stefan Timmermans, Luna White, and Joshua Whitford.

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.

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.