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Did compulsory busing services in the 1970s raise the school achievement of homeless minority youth? Is having a college degree increase an individual's job market earnings? Will earning a college degree increase an individual's job market earnings? Did the use of the butterfly ballot in several Florida counties in the 2000 presidential election cost Al Gore votes? This book includes a model and a set of methods for causal effect estimation that social scientists can use to solve causal problems such as these.
Source link: https://doi.org/10.1017/cbo9780511804564
Both the potential outcome model and causal graphs were first introduced; after that, conditioning methods, such as matching and regression, are introduced from a potential outcomes viewpoint; and finally, regression methods, such as matching and regression, are first introduced. Throughout the book, the importance of causal effect heterogeneity is stressed, and the need for deep causal explanation by mechanisms is discussed.
Source link: https://doi.org/10.1017/cbo9781107587991
Abstract Background The counterfactual or prospective outcome model has become more common for causal inference in epidemiological and medical studies. When estimating causal effects, a variety of conceptual as well as practical issues are addressed. According to several statistical methods, it has been claimed that the counterfactual model of causal effects captures the key aspects of causality in health sciences and extends to several statistical methods. Introduction to medicine and epidemiology Counterfactuals are the source of causal inference.
Source link: https://doi.org/10.1186/1471-2288-5-28
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