This document discusses causal models and their use in social science research. It covers several key topics: 1) Causal models are used to explain social phenomena by modeling causal mechanisms. They involve defining variables, structuring relationships, and testing models. 2) Causal models differ from systemic models in their treatment of exogeneity and covariate sufficiency. Multi-level models address social hierarchies by including individual and group level variables. 3) Mixed mechanism models recognize that multiple social, economic, and psychological factors can interact causally. Theoretical plausibility of relationships is important. 4) Causal relationships in social models may exhibit regularities across time or populations, though testing invariance poses challenges around reference

1. Causal mechanisms
from causal models
Federica Russo
Center Leo Apostel, VUB
Centre for Reasoning, Kent

2. Overview
Causal models: a baseline view
Causal vs Systemic
The role of Exogeneity and Covariate Sufficiency
Multi-level
A statistical expression of social hierarchies
Mixed Mechanism
Theoretical plausibility of role-functions
Social Regularities
Invariance of the ‘arrangement’
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3. CAUSAL MODELS
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4. A tradition of scientific enquiry
Quetelet, Durkheim, Wright, …,
Blalock, Duncan, Simon, …,
Haavelmo, Koopmans, Wold, …,
SGS, Pearl, Woodward, …
To explain a (social) phenomenon
we have to model mechanisms
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5. A step-wise methodology
1. Define the research question, the population of
reference, the context
2. Give structure to a multivariate probability
distribution including all the variables
3. Translate the conceptual model into an
operational model
4. Test the model and draw conclusions
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6. Self-rated health in Baltic countries 1994-1999 6

7. CAUSAL VS SYSTEMIC
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8. Exogeneity tests
“Causes generated outside the model”
Rather: A condition of separation of inference
In the recursive decomposition
P(Y)= P(X1) P(X3) P(X2|X3) … P(Y|X2, X3)
we (aim to) separate causes from effects
Covariate sufficiency
We assume that all and only the relevant variables have
been included in the model
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9. Health system and mortality in Spain
(causal)
X1 12 X2
Economic Social development
development 2
Y
13 Mortality
4
X3 34 X4
Sanitary Use of sanitary
infrastructures infrastructures
X5
54 Age structure
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10. Health systems
and mortality
in Spain
(systemic)
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11. MULTI-LEVEL
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12. Social hierarchies
Individuals / family / local population / national
population
Firms / regional market / national market / global
market
Pupils / classes / schools / school systems
…

13. Approaches and dangers
Holism
The system as a whole determines how the parts behave
Individualism
Social phenomena and behaviours are explained through
individual decisions and actions
Atomistic fallacy
Wrongly infer a relation between units at a higher level of
analysis from units at a lower level of analysis
Ecological fallacy
Draw inferences about relations between individual level
variables based on the group level data
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14. Multi-level models
Yij 0j x
1 j ij 2 zj ij
response variable at the
individual level
explanatory variable at the individual level
explanatory variable at the group level
i: index for the individuals
j: index for the group
these 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)

16. MIXED MECHANISMS
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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
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18. Not just ‘statistical’
We can add any variable we like in a causal model
But we must justify the role-function of each factor in
the mechanism
Even more in mixed-mechanisms
Theoretical plausibility backs up statistical modelling
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19. SOCIAL REGULARITIES
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20. Regularities in causal models
Humean 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
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21. A problem of testing
Testwhether 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
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22. To sum up
Large part of social research makes use of causal models
These models enhance our understanding of the social by
modelling mechanisms
Specific features of causal models link to bigger debates
Causal vs Systemic
Hierarchies
Theoretical plausibility
Regularities in the social
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23. To conclude
The modelling of mechanisms is of great help to
explanation and understanding
Mechanisms that come out of causal models are
epistemic – mechanism schemata
Up to social theory to tell us how ontic these
mechanisms are
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24. Further readings
Russo 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.
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