Editors:
- Estimation of causal relationships based on non-experimental data in population studies
- Comprehensive discussion of available techniques
- Contributions by the leading scholars in the field
Part of the book series: The Springer Series on Demographic Methods and Population Analysis (PSDE, volume 23)
Buy it now
Buying options
Tax calculation will be finalised at checkout
Other ways to access
This is a preview of subscription content, log in via an institution to check for access.
Table of contents (10 chapters)
-
Front Matter
-
Back Matter
About this book
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.
Reviews
From the reviews:
“This book contains a selection of nine of the contributions to a conference with the same title as the book, held in Vienna in December 2006. … The editors should be complimented for providing a clear focus on the econometric analysis of aspects of demographic behavior by issuing all these papers in a single volume. … Their appearance in a single volume will hopefully inspire more interest in economic demography among economists and more interest in econometric issues among demographers.” (Jan M. Hoem, European Journal of Population, Vol. 26, October, 2010)Editors and Affiliations
-
Universität Bamberg LS for Bevölkerungswissenschaft, Germany
Henriette Engelhardt
-
Dept. Sociology Population Studies Center, University of Pennsylvania, Philadelphia, USA
Hans-Peter Kohler
-
Österreichischen Akademie der Wissenschaften Inst. Demographie, Austria
Alexia Fürnkranz-Prskawetz
Bibliographic Information
Book Title: Causal Analysis in Population Studies
Book Subtitle: Concepts, Methods, Applications
Editors: Henriette Engelhardt, Hans-Peter Kohler, Alexia Fürnkranz-Prskawetz
Series Title: The Springer Series on Demographic Methods and Population Analysis
DOI: https://doi.org/10.1007/978-1-4020-9967-0
Publisher: Springer Dordrecht
eBook Packages: Humanities, Social Sciences and Law, Social Sciences (R0)
Copyright Information: Springer Science+Business Media B.V. 2009
Hardcover ISBN: 978-1-4020-9966-3Published: 08 May 2009
Softcover ISBN: 978-90-481-8232-9Published: 28 October 2010
eBook ISBN: 978-1-4020-9967-0Published: 05 May 2009
Series ISSN: 1877-2560
Series E-ISSN: 2215-1990
Edition Number: 1
Number of Pages: VIII, 252
Topics: Demography, Methodology of the Social Sciences, Sociology, general, Population Economics, Statistics for Social Sciences, Humanities, Law, Social Sciences, general