publication . Article . 2007

confounding and control

Guillaume Wunsch;
Open Access
  • Published: 01 Feb 2007 Journal: Demographic Research, volume 16, pages 97-120 (eissn: 1435-9871, Copyright policy)
  • Publisher: Max Planck Institute for Demographic Research
This paper deals both with the issues of confounding and of control, as the definition of a confounding factor is far from universal and there exist different methodological approaches, ex ante and ex post, for controlling for a confounding factor. In the first section the paper compares some definitions of a confounder given in the demographic and epidemiological literature with the definition of a confounder as a common cause of both treatment/exposure and response/outcome. In the second section, the paper examines confounder control from the data collection viewpoint and recalls the stratification approach for ex post control. The paper finally raises the iss...
free text keywords: confounding, control, structural modelling, Demography, Data collection, Economics, Common cause and special cause, Confounding, Ex-ante, Econometrics, jel:J1, jel:Z0, control, structural modelling, Demography. Population. Vital events, HB848-3697
48 references, page 1 of 4

Anderson S., A. Auquier, W.W. Hauck, D. Oakes, W. Vandaele, and H.I. Weisberg (1980). Statistical Methods for Comparative Studies. New York, Wiley, 289 p.

Angrist J.D., G.W. Imbens, and D.B. Rubin (1996). Identification of causal effects using instrumental variables, Journal of the American Statistical Association, 91(434), 444-455. [OpenAIRE]

Angrist J.D. and A.B. Krueger (2001). Instrumental variables and the search for identification: from supply and demand to natural experiments, Journal of Economic Perspectives, 15(4), 69-85. [OpenAIRE]

Bartecchi C.E., T.D. MacKenzie, and R.W. Schreir (1995). The global tobacco epidemic, Scientific American, 272(5), 26-33.

Best N. and P. Green (2005). Structure and uncertainty, Significance, 2(4), 177-181.

Blalock H.M. (1961). Causal Inferences in Nonexperimental Research. Chapel Hill, The University of North Carolina Press, 200 p.

Bollen K.A. (1989). Structural Equations with Latent Variables. Wiley, New York, 514 p.

Brown S.R. and L.E. Melamed (1990). Experimental design and analysis. Newbury Park, Sage Publications, 86 p.

Chalmers I. and R. Matthews (2006). What are the implications of optimism bias in clinical research?, The Lancet, 367(9509), 449-450.

Coale A.J. (1973). The demographic transition reconsidered. Proceedings of the International Population Conference, Volume 1, IUSSP, Liège, 53-72.

Courgeau D. (2002). Réflexions sur Régression et analyse géométrique des données, Henry Rouanet et al., personal communication, 8 p.

Courgeau D. (2003). From the macro-micro opposition to multilevel analysis in demography, chapter 2 in: D. Courgeau (ed.). Methodology and Epistemology of Multilevel Analysi., METHODOS Series N° 2, Dordrecht, Kluwer, 43-91. [OpenAIRE]

Cox D.R. and N. Wermuth (2004). Causality: a statistical view, International Statistical Review, 72(3), 285-305.

Dawid A.P. (2002).Influence diagrams for modelling and inference, International Statistical Review, 70, 161-189.

Elwood J.M. (1988). Causal Relationships in Medicine. Oxford, Oxford University Press, 332 p.

48 references, page 1 of 4
Powered by OpenAIRE Research Graph
Any information missing or wrong?Report an Issue