• Like
  • Save
Russo rotterdam2012
Upcoming SlideShare
Loading in...5

Thanks for flagging this SlideShare!

Oops! An error has occurred.



Published in Education , Technology
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
    Be the first to like this
No Downloads


Total Views
On SlideShare
From Embeds
Number of Embeds



Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

    No notes for slide


  • 1. Causal mechanismsfrom causal models Federica Russo Center Leo Apostel, VUB Centre for Reasoning, Kent
  • 2. OverviewCausal models: a baseline viewCausal vs Systemic The role of Exogeneity and Covariate SufficiencyMulti-level A statistical expression of social hierarchiesMixed Mechanism Theoretical plausibility of role-functionsSocial Regularities Invariance of the ‘arrangement’ 2
  • 4. A tradition of scientific enquiryQuetelet, Durkheim, Wright, …,Blalock, Duncan, Simon, …,Haavelmo, Koopmans, Wold, …,SGS, Pearl, Woodward, …To explain a (social) phenomenonwe have to model mechanisms 4
  • 5. A step-wise methodology1. Define the research question, the population of reference, the context2. Give structure to a multivariate probability distribution including all the variables3. Translate the conceptual model into an operational model4. Test the model and draw conclusions 5
  • 6. Self-rated health in Baltic countries 1994-1999 6
  • 8. Exogeneity tests“Causes generated outside the model”Rather: A condition of separation of inferenceIn the recursive decompositionP(Y)= P(X1) P(X3) P(X2|X3) … P(Y|X2, X3)we (aim to) separate causes from effects Covariate sufficiencyWe assume that all and only the relevant variables have been included in the model 8
  • 9. Health system and mortality in Spain (causal) X1 12 X2 Economic Social developmentdevelopment 2 Y 13 Mortality 4 X3 34 X4 Sanitary Use of sanitaryinfrastructures infrastructures X5 54 Age structure 9
  • 10. Health systemsand mortalityin Spain(systemic) 10
  • 11. MULTI-LEVEL 11
  • 12. Social hierarchiesIndividuals / family / local population / national populationFirms / regional market / national market / global marketPupils / classes / schools / school systems…
  • 13. Approaches and dangersHolism The system as a whole determines how the parts behaveIndividualism Social phenomena and behaviours are explained through individual decisions and actionsAtomistic fallacy Wrongly infer a relation between units at a higher level of analysis from units at a lower level of analysisEcological fallacy Draw inferences about relations between individual level variables based on the group level data 13
  • 14. Multi-level models Yij 0j x 1 j ij 2 zj ijresponse variable at theindividual level explanatory variable at the individual level explanatory variable at the group leveli: index for the individualsj: index for the groupthese vary depending on the group Errors are independent at each level and between levels
  • 15. Farmers’ migration in Norway Data from the Norwegian population registry (since 1964) and from two national censuses (1970 and 1980) Aggregate model and individual model show opposite results: Aggregate—regions with more farmers are those with higher rates of migrations; Individual—in a same region migration rates are higher for non-farmers than for farmers Reconciliation: multi-level model aggregate characteristics (e.g. the percentage of farmers) explain individual behaviour (e.g. migrants’ behaviour)
  • 17. Not just ‘social’Socio-economic, health, psychological factors may act in a same mechanism Mother’s education and child survival in developing countries Child obesity and socio-psychological development 17
  • 18. Not just ‘statistical’We can add any variable we like in a causal modelBut we must justify the role-function of each factor in the mechanism Even more in mixed-mechanismsTheoretical plausibility backs up statistical modelling 18
  • 20. Regularities in causal modelsHumean regularities? (constant conjunction)Rather: Repetitions of the same causal structure, either in time or given the same causally relevant factors Tested through invariance properties Change-relating relations that have a stable parametrisation in chosen sub-populations 20
  • 21. A problem of testingTestwhether relations are regular (in the invariance sense)Information needed to establish generic causal relations‘Generic’ comes into degrees: Relative to the population of reference Open question about external validity 21
  • 22. To sum upLarge part of social research makes use of causal modelsThese models enhance our understanding of the social by modelling mechanismsSpecific features of causal models link to bigger debates Causal vs Systemic Hierarchies Theoretical plausibility Regularities in the social 22
  • 23. To concludeThe modelling of mechanisms is of great help to explanation and understandingMechanisms that come out of causal models are epistemic – mechanism schemataUp to social theory to tell us how ontic these mechanisms are 23
  • 24. Further readingsRusso F. (2009). Causality and Causal Modelling in the Social Sciences. Measuring Variations.Springer.Russo F. (2010). Are causal analysis and system analysis compatible approaches?, International Studies in Philosophy of Science, 24(1), 67-90.Russo F. (2011). Causal webs in epidemiology, Paradigmi, Special Issue on the Philosophy of Medicine, XXXIX (1), 67- 98.Russo F. (2012). A non-manipulationist account of invariance. Unpublished manuscript. 24