Structural Modelling, Exogeneity, and Causality

Mouchart, Michel; Russo, Federica; Wunsch, Guillaume

doi:10.1007/978-1-4020-9967-0_4

Cited by 20 publications

(19 citation statements)

References 17 publications

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“…As we have argued elsewhere (e.g. Mouchart, Russo, and Wunsch, 2009;Mouchart and Russo, 2011;Russo, forthcoming), a structural model should be based on the best available knowledge one has of the field; all postulated relations should be accounted for. In particular, it should incorporate those variables deemed to be responsible for possible assignment bias.…”

Section: Discussionmentioning

confidence: 99%

“…drop-outs (Mealli and Rubin, 2002;Frangakis and Rubin, 2002). Moreover, from a strict statistical point of view, placebo/nocibo effects should be considered as -residual‖, or, better said, -unexplained‖ effects (see Bouckaert and Mouchart, 2001), if one recognises that a statistical model provides a partial explanation only, the stochastic component corresponding to what is not explained by the model (see Wunsch 2009, and.…”

Section: Randomizationmentioning

confidence: 99%

See 1 more Smart Citation

Inferring Causality through Counterfactuals in Observational Studies - Some Epistemological Issues

Russo

Wunsch

2011

Bulletin of Sociological Methodology/Bulletin de Méthodologie S

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This paper contributes to the debate on the virtues and vices of counterfactuals as a basis for causal inference. The goal is to put the counterfactual approach in an epistemological perspective. We discuss a number of issues, ranging from its non-observable basis to the parallelisms drawn between the counterfactual approach in statistics and in philosophy. We argue that the question is not to oppose or to endorse the counterfactual approach as a matter of principle, but to decide what modelling framework is best to adopt depending on the research context.

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Section: Discussionmentioning

confidence: 99%

Section: Randomizationmentioning

confidence: 99%

Inferring Causality through Counterfactuals in Observational Studies - Some Epistemological Issues

Russo

Wunsch

2011

Bulletin of Sociological Methodology/Bulletin de Méthodologie S

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“…On the contrary, a major advantage of SEM is that it gives a precise picture of one"s hypothetical causal structure, distinguishing the network of paths among variables, both direct and indirect, and separating confounders from intervening variables. As regards causality, in addition to being identified and fitting the data adequately, a SEM should be congruent with background knowledge and structurally stable (Mouchart et al 2009). As the analysis is restricted to the data available in the Norbalt surveys, latent confounders may however be present and bias the results.…”

Section: The Statistical Modelmentioning

confidence: 99%

Self-Rated Health in the Baltic Countries, 1994–1999

Gaume¹,

Wunsch²

2010

Eur J Population

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International audienceNumerous studies have examined the explanations of mortality fluctuations in the former USSR during the last decade of the twentieth century—a time of considerable political and socio-economic changes—but fewer studies have considered the health of these populations during this period. Using individual data from the Norbalt surveys held in 1994 and 1999 in the three Baltic countries, we examine the determinants of self-rated health in the three countries and for the two periods, by way of Bayesian structural equation modelling and directed acyclic graphs. The model takes into account, as possible determinants, alcohol consumption, physical health, psychological distress, education, locus of control, and social support. A major result is the remarkable stability of the model's parameters whatever the country, year, gender, ethnicity, or age-group. Particular attention is given to the role of alcohol consumption and to the association observed between better self-assessed health and higher drinking

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“…More technical and precise definitions of causal model are of course possible. See for instance Wunsch (1988) and Freedman (2005) for an accessible introduction to causal modelling and statistics, Mouchart et al (2008) and Russo (2008) for a critical evaluation of them. For the purpose of the present paper we can keep technicalities to a minimum.…”

Section: Quantitative Causal Analysismentioning

confidence: 99%