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Kategorie szczegółowe BISAC

Causal Analysis in Population Studies: Concepts, Methods, Applications

ISBN-13: 9781402099663 / Angielski / Twarda / 2009 / 251 str.

Henriette Engelhardt; Hans-Peter Kohler; Alexia Fa1/4rnkranz-Prskawetz
Causal Analysis in Population Studies: Concepts, Methods, Applications Engelhardt, Henriette 9781402099663 Springer - książkaWidoczna okładka, to zdjęcie poglądowe, a rzeczywista szata graficzna może różnić się od prezentowanej.

Causal Analysis in Population Studies: Concepts, Methods, Applications

ISBN-13: 9781402099663 / Angielski / Twarda / 2009 / 251 str.

Henriette Engelhardt; Hans-Peter Kohler; Alexia Fa1/4rnkranz-Prskawetz
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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.

Kategorie:
Nauka, Socjologia i społeczeństwo
Kategorie BISAC:
Social Science > Ludność i demografia
Social Science > Statistics
Business & Economics > Economics - General
Wydawca:
Springer
Seria wydawnicza:
Springer Series on Demographic Methods and Population Analys
Język:
Angielski
ISBN-13:
9781402099663
Rok wydania:
2009
Wydanie:
2009
Numer serii:
000320417
Ilość stron:
251
Waga:
0.54 kg
Wymiary:
23.39 x 15.6 x 1.6
Oprawa:
Twarda
Wolumenów:
01
Dodatkowe informacje:
Wydanie ilustrowane

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)

1: Causal analysis in population studies: Henriette Engelhardt, Hans Peter Kohler and Alexia Prskawetz.- 2: Issues in the estimation of causal effects in population research, with an application to the effects of teenage childbearing: Robert A. Moffitt.- 3: Sequential potential outcome models to analyze the effects of fertility on labor market outcomes: Michael Lechner.- 4: Structural modelling, exogeneity, and causality: Michel Mouchart, Federica Russo and Guillaume Wunsch.- 5: Causation as a generative process. The elaboration of an idea for the social sciences and an application to an analysis of an interdependent dynamic social system: Hans-Peter Blossfeld.- 6: Instrumental variable estimation for duration date: Govert E. Bijwaard.- 7: Female labour participation with concurrent demographic processes: an estimation for italy: gustavo De Santis and Antonino Di Pino.- 8: New estimates on the effect of parental separation on child health: Shirley H. Liu and Frank Heiland.- 9: Assessing the causal effect of childbearing on household income in Albania: Francesca Francavilla and Alessandra Mattei.- 10: Causation and its discontents: Herbert L. Smith

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.

Kohler, Hans-Peter Hans-Peter Kohler received his M.A. in Demography ... więcej >


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