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Published by Campus Verlag 10.2012., 2012
ISBN 10: 3593397536 ISBN 13: 9783593397535
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Condition: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | This book is the outcome of a project that started with the organisation of theTopicalWorkshoponżAgent-BasedComputationalModelling. AnInst- ment for Analysing Complex Adaptive Systems in Demography, Economics and Environmentż at the Vienna Institute of Demography, December 4-6, 2003. The workshop brought together scholars from several disciplines, all- ing both for serious scienti?c debate and for informal conversation over a cup co?ee or during a visit to the wonderfulmuseums of Vienna. One of the nicest features of Agent-Based Modelling is indeed the opportunity that scholars ?nd a common language and discuss from their disciplinary perspective, in turn learning from other perspectives. Given the success of the meeting, we found it important to pursue the purpose of collecting these interdisciplinary contributions in a volume. In order to ensure the highest scienti?c standards for the book, we decided that all the contributions (with the sole exception of the introductory chapter) should have been accepted conditional on peer reviews. Generoushelpwasprovidedbyreviewers,someofwhomwereneither directly involved in the workshop nor in the book. All this would not have been possible without the funding provided by the Complex Systems N- work of Excellence (Exystence) funded by the European Union, the Vienna Institute of Demography of the Austrian Academy of Sciences, Universit` a Bocconi, and ARC Systems Research GmbH, and the help of the wonderful sta? ofthe Vienna Institute of Demography(in particular,Ani Minassianand Belinda Aparicio Diaz). Agent-Based Modelling is important, interesting and also funżwe hope this book contributes to showing that. Milano Francesco C.
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Taschenbuch. Condition: Neu. Causal Analysis in Population Studies | Concepts, Methods, Applications | Henriette Engelhardt (u. a.) | Taschenbuch | The Springer Series on Demographic Methods and Population Analysis | viii | Englisch | 2010 | Springer | EAN 9789048182329 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Taschenbuch. Condition: Neu. 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.
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Buch. Condition: Neu. 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.
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Condition: New. 1st ed. 2019 edition NO-PA16APR2015-KAP.