2010, ISBN: 9048182328

[EAN: 9789048182329], Neubuch, [SC: 0.0], [PU: Springer Netherlands], CAUSALANALYSIS; CAUSALEFFECTS; COUNTERFACTUALAPPROACH; DEMOGRAPHICPROCESSES; DEMOGRAPHY; ECONOMETRICS; NON-EXPERIMENT… Meer...

[EAN: 9789048182329], Neubuch, [SC: 0.0], [PU: Springer Netherlands], CAUSALANALYSIS; CAUSALEFFECTS; COUNTERFACTUALAPPROACH; DEMOGRAPHICPROCESSES; DEMOGRAPHY; ECONOMETRICS; NON-EXPERIMENTALDATA; POPULATIONRESEARCH; POPULATIONSTUDIES; SOCIALSCIENCES, Druck auf Anfrage Neuware - Printed after ordering - The central aim of many studies in population research and demography is to explain cause-effect relationships among variables or events. For decades, population scientists have concentrated their efforts on estimating the 'causes of effects' by applying standard cross-sectional and dynamic regression techniques, with regression coefficients routinely being understood as estimates of causal effects. The standard approach to infer the 'effects of causes' in natural sciences and in psychology is to conduct randomized experiments. In population studies, experimental designs are generally infeasible.In population studies, most research is based on non-experimental designs (observational or survey designs) and rarely on quasi experiments or natural experiments. Using non-experimental designs to infer causal relationships-i.e. relationships that can ultimately inform policies or interventions-is a complex undertaking. Specifically, treatment effects can be inferred from non-experimental data with a counterfactual approach. In this counterfactual perspective, causal effects are defined as the difference between the potential outcome irrespective of whether or not an individual had received a certain treatment (or experienced a certain cause). The counterfactual approach to estimate effects of causes from quasi-experimental data or from observational studies was first proposed by Rubin in 1974 and further developed by James Heckman and others.This book presents both theoretical contributions and empirical applications of the counterfactual approach to causal inference., Books<

ISBN: 9789048182329

[ED: Taschenbuch], [PU: Springer Netherlands], Neuware - The central aim of many studies in population research and demography is to explain cause-effect relationships among variables or … Meer...

[ED: Taschenbuch], [PU: Springer Netherlands], Neuware - The central aim of many studies in population research and demography is to explain cause-effect relationships among variables or events. For decades, population scientists have concentrated their efforts on estimating the 'causes of effects' by applying standard cross-sectional and dynamic regression techniques, with regression coefficients routinely being understood as estimates of causal effects. The standard approach to infer the 'effects of causes' in natural sciences and in psychology is to conduct randomized experiments. In population studies, experimental designs are generally infeasible.In population studies, most research is based on non-experimental designs (observational or survey designs) and rarely on quasi experiments or natural experiments. Using non-experimental designs to infer causal relationships-i.e. relationships that can ultimately inform policies or interventions-is a complex undertaking. Specifically, treatment effects can be inferred from non-experimental data with a counterfactual approach. In this counterfactual perspective, causal effects are defined as the difference between the potential outcome irrespective of whether or not an individual had received a certain treatment (or experienced a certain cause). The counterfactual approach to estimate effects of causes from quasi-experimental data or from observational studies was first proposed by Rubin in 1974 and further developed by James Heckman and others.This book presents both theoretical contributions and empirical applications of the counterfactual approach to causal inference.- Besorgungstitel - vorauss. Lieferzeit 3-5 Tage., DE, [SC: 0.00], Neuware, gewerbliches Angebot, 235x155x15 mm, 260, [GW: 400g], Banküberweisung, Offene Rechnung, Kreditkarte, PayPal, Offene Rechnung (Vorkasse vorbehalten), Internationaler Versand<

2010, ISBN: 9789048182329

[ED: Softcover], [PU: Springer / Springer Netherlands], The central aim of many studies in population research and demography is to explain cause-effect relationships among variables or e… Meer...

[ED: Softcover], [PU: Springer / Springer Netherlands], The central aim of many studies in population research and demography is to explain cause-effect relationships among variables or events. For decades, population scientists have concentrated their efforts on estimating the 'causes of effects' by applying standard cross-sectional and dynamic regression techniques, with regression coefficients routinely being understood as estimates of causal effects. The standard approach to infer the 'effects of causes' in natural sciences and in psychology is to conduct randomized experiments. In population studies, experimental designs are generally infeasible. In population studies, most research is based on non-experimental designs (observational or survey designs) and rarely on quasi experiments or natural experiments. Using non-experimental designs to infer causal relationships-i.e. relationships that can ultimately inform policies or interventions-is a complex undertaking. Specifically, treatment effects can be inferred from non-experimental data with a counterfactual approach. In this counterfactual perspective, causal effects are defined as the difference between the potential outcome irrespective of whether or not an individual had received a certain treatment (or experienced a certain cause). The counterfactual approach to estimate effects of causes from quasi-experimental data or from observational studies was first proposed by Rubin in 1974 and further developed by James Heckman and others. This book presents both theoretical contributions and empirical applications of the counterfactual approach to causal inference. 2010. viii, 252 S. VIII, 252 p. 235 mm Versandfertig in 6-10 Tagen, DE, [SC: 0.00], Neuware, gewerbliches Angebot, Offene Rechnung (Vorkasse vorbehalten)<

ISBN: 9789048182329

The central aim of many studies in population research and demography is to explain cause-effect relationships among variables or events. For decades, population scientists have concentra… Meer...

The central aim of many studies in population research and demography is to explain cause-effect relationships among variables or events. For decades, population scientists have concentrated their efforts on estimating the ‘causes of effects’ by applying standard cross-sectional and dynamic regression techniques, with regression coefficients routinely being understood as estimates of causal effects. The standard approach to infer the ‘effects of causes’ in natural sciences and in psychology is to conduct randomized experiments. In population studies, experimental designs are generally infeasible. In population studies, most research is based on non-experimental designs (observational or survey designs) and rarely on quasi experiments or natural experiments. Using non-experimental designs to infer causal relationships—i.e. relationships that can ultimately inform policies or interventions—is a complex undertaking. Specifically, treatment effects can be inferred from non-experimental data with a counterfactual approach. In this counterfactual perspective, causal effects are defined as the difference between the potential outcome irrespective of whether or not an individual had received a certain treatment (or experienced a certain cause). The counterfactual approach to estimate effects of causes from quasi-experimental data or from observational studies was first proposed by Rubin in 1974 and further developed by James Heckman and others. This book presents both theoretical contributions and empirical applications of the counterfactual approach to causal inference. Trade Books>Trade Paperback>Social Sciences>Sociology>Sociology, Springer Netherlands Core >1<

2010, ISBN: 9048182328

[EAN: 9789048182329], Neubuch, [PU: Springer Netherlands], CAUSALANALYSIS CAUSALEFFECTS COUNTERFACTUALAPPROACH DEMOGRAPHICPROCESSES DEMOGRAPHY ECONOMETRICS NON-EXPERIMENTALDATA POPULATION… Meer...

[EAN: 9789048182329], Neubuch, [PU: Springer Netherlands], CAUSALANALYSIS CAUSALEFFECTS COUNTERFACTUALAPPROACH DEMOGRAPHICPROCESSES DEMOGRAPHY ECONOMETRICS NON-EXPERIMENTALDATA POPULATIONRESEARCH POPULATIONSTUDIES SOCIALSCIENCES SOZIOLOGIE POLITISCHE CAUSAL ANALYSIS EFFECTS, Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Estimation of causal relationships based on non-experimental data in population studiesComprehensive discussion of available techniquesContributions by the leading scholars in the fieldThe central aim of many studies in populatio., Books<