Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/309223 
Year of Publication: 
2021
Citation: 
[Journal:] Comparative Population Studies (CPoS) [ISSN:] 1869-8999 [Volume:] 46 [Year:] 2021 [Pages:] 69-96
Publisher: 
Bundesinstitut für Bevölkerungsforschung (BiB), Wiesbaden
Abstract: 
We aim to give an overview of the state of the art of causal analysis of demographic issues related to morbidity and mortality. We will systematically introduce strategies to identify causal mechanisms, which are inherently linked to panel data from observational surveys and population registers. We will focus on health and mortality, and on the issues of unobserved heterogeneity and reverse causation between health and (1) retirement, (2) socio-economic status, and (3) characteristics of partnership and fertility history. The boundaries between demographic research on mortality and morbidity and the neighbouring disciplines epidemiology, public health and economy are often blurred. We will highlight the specific contribution of demography by reviewing methods used in the demographic literature. We classify these methods according to important criteria, such as a design-based versus model-based approach and control for unobserved confounders. We present examples from the literature for each of the methods and discuss the assumptions and the advantages and disadvantages of the methods for the identification of causal effects in demographic morbidity and mortality research. The differentiation between methods that control for unobserved confounders and those that do not reveal a fundamental difference between (1) methods that try to emulate a randomised experiment and have higher internal validity and (2) methods that attempt to achieve conditional independence by including all relevant factors in the model. The latter usually have higher external validity and require more assumptions and prior knowledge of relevant factors and their relationships. It is impossible to provide a general definition of the sort of validity that is more important, as there is always a trade-off between generalising the results to the population of interest and avoiding biases in the estimation of causal effects in the sample. We hope that our review will aid researchers in identifying strategies to answer their specific research question.
Subjects: 
Causality
Health
Methods
Mortality
Panel data
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-sa Logo
Document Type: 
Article

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.