Causality, stability, social sciences

1. Causal assessment and
the question of stability
Federica Russo
Philosophy | Humanities | Amsterdam
russofederica.wordpress.com | @federicarusso

2. 2
philosophy of causality
philosophical approaches to
causality
analysis of causal language and
intuitions
analysis of scientific practice
scientific problems about
causality: inference, prediction,
explanation, control, reasoning
here: inference, reasoning
philosophical questions about
causality: metaphysics,
epistemology, methodology,
semantics, use
here: methodology,
epistemology

3. Overview
Causal assessment
Single case causal relations
Empirical generalisations
Causal modelling in social science
Stability
Invariance under intervention
Invariance across changes of the environment
Invariance of what?
Joint variations
Regular associations
Why extending the concept?
3

4. CAUSAL ASSESSMENT
4

5. Single case causal relations
Failing to water my plant made it die
Alice’s gastric ulcer is due to Helicobacter Pylori
The year of the Fire Horse caused fertility drop in Japan
in 1966
The financial bubble caused the 2008 economic crisis
…
5

6. Empirical generalisations
Smoking causes cancer / heath disease / …
Effects of mobile phones radiations
Socio-economic policies increase social mobility
Causes and effects of solar storms
…
6

7. Causal modelling (in social science)
Formulate causal hypotheses
Build the statistical model
Test the model
Conclude to the validity/invalidity of the model
Role of background knowledge
Statistical and causal assumptions
Specific concepts involved
7

8. What are the causes of self-rated health in
the Baltic countries in the ‘90s?
X Y
Joint probability distribution
P(Ed, Soc, Phy, Loc, Psy, Alc, Self)
Recursive decomposition:
P(Self|Alc, Psy, Loc, Phy)
P(Alc|Ed, Psy, Phy)
P(Psy|Loc, Soc, Phy)
P(Loc|Ed)
P(Phy) P(Soc) P(Ed)
Health survey in the Baltic countries
8
What does testing
stability mean?

9. STABILITY
9

10. The ‘received’ view
Invariance under intervention
Woodward and other manipulationist theorists
X causes Y if, wiggling X, Y accordingly wiggles, and
the relation between X and Y remains stable
Where does this apply?
Anywhere: physics, economics, biology, …
Conceptual analysis? Metaphysics? Methodology?
Under debate …
10
X  Y

11. The ‘new course’
11
Invariance
across changes …
Changes in the effect-factor,
due to interventions
on the cause-factor
Changes of the environment,
i.e. across appropriate
partitions of the data set
Experimental
contexts
Observational
contexts

12. Changes in the effect-variable
P V = n R T
Pressure
Volume
Number
of moles
Gas
constant
Temperature
12

13. Woodward’s conditions
Interventions I on cause-factor X:
Change in X (cause) is totally due to I
Change in Y (effect) totally due to change in X
I is not correlated with other possible causes of Y
If all this holds – and that’s a big if –
empirical generalisations are causal
13

14. Changes in the environment
Environments are
appropriate partitions of the population of reference
Age groups
Socio-economic conditions
Exposures
…
Invariance is a test for stability across environments
Of the parametrisation (numerical values)
Of the causal structure (arrangements)
14

15. Invariance in early econometrics
The beginning of contemporary causal modelling:
Modelling and testing economic structures
Marschak, Frisch, Haavelmo, Simon:
Learning invariance properties of the model or economic
system from data
Test whether hypothetical variations (theoretical level) in
economic structure are actually invariant (empirical level)
Invariance of the parametrisation of the system
15

16. Simon: causal ordering and invariance
Causal ordering is a property of models that is
invariant with respect to interventions within the
model, and structural equations are equations that
correspond to specified possibilities of interventions.
(1953, p66)
The concepts to be defined all refer to a model—a
system of equations— and not to the ‘real’ world the
model purports to describe. (1953, p51)
16

17. Simon: causal ordering and invariance
Causal ordering is a property of models that is
invariant with respect to interventions within the
model, and structural equations are equations that
correspond to specified possibilities of interventions.
(1953, p66)
The concepts to be defined all refer to a model—a
system of equations— and not to the ‘real’ world the
model purports to describe. (1953, p51)
17
Modifications
of
environments

18. What are the causes of self-rated health
in the Baltic countries in the ‘90s? 18

19. What environments?
1994 and 1999 data sets
Estonian Males; Estonian Females
Latvian Males; Latvia Females
Lithuanian Males; Lithuanian Females
Age groups (18–29, 30–44, 45–59, 60+)
Autochthons and other (mainly Russians)
Background knowledge looms large …
19

20. Stability of the
parametrisation
Check parameter stability for
each relationship, within the
environments
Baltic study:
parametrisation is stable for
most environments
•Time-frames,
•Gender,
•Ethnical groups,
•Age
20

21. Impact of alcohol consumption on self-rated health
Sign of parameter and numerical value are stable
21
Female Male
Estonia −0.094 −0.039
Latvia −0.181 −0.054
Lithuania −0.157 −0.068

22. Parametrisation of
the causal structure
is stable for each
environment
Stability of the
causal structure
The structure
(= arrangement of variables)
is the same for
3 Baltic countries studied
Men and women
Ethnic groups
…
22

23. Parametrisation and causal structure
They are clearly not independent
If stability of parametrisation fails,
we are led to rethink causal
structure, at least for some sub-
populations
Question:
How homogenous /
heterogeneous is the population
of reference?
23

24. INVARIANCE OF WHAT?
24

25. Zoom in ‘invariance’
Causal assessment
Causal modelling
Stability
Invariance across changes of the environment
25

26. Causal assessment
Causal modelling
Model building / testing
Variation, regularity
Recursive decomposition, invariance, exogeneity, …
Zoom out ‘model building’
26

27. What has to be invariant?
Joint variations that are regular enough

28. Detect joint variations within and
between variables
Visibility: no variation, no statistical analysis
Woodward: change-relating relations
How regular are joint variations?
Dependencies  Strength of association
How strong: frequency of occurrence
How stable are dependencies?
Invariance across partitions of the
population
Parametrisation and structure
Variation
Regularity
Invariance
28

29. Model building / testing
Invariance
Regularity
Variation
29

30. WHY EXTENDING INVARIANCE?
30

31. Weak, strong, or
differently implemented

32. […] the authors note that some association appears between smoking and lung cancer in every well-
designed study on sufficiently large and representative populations with which they are familiar. There is
evidence of a higher frequency of lung cancer among smokers than among
nonsmokers, when potentially confounding variables are controlled for, among both men and
women, among people of different genetic backgrounds, across different diets,
different environments, and different socioeconomic conditions […].
The precise level and quantitative details of the association do vary, for example, the incidence of lung
cancer among smokers is higher in lower socioeconomic groups, but the fact that there is some association
or other is stable or robust across a wide variety or different groups and background circumstances. […]
Thus, although Cornfield et al. do not exhibit a precise deterministic or probabilistic generalization that
is invariant across different circumstances [meaning: across interventions] the cumulative impact of their
evidence is to show that the relationship between smoking and lung cancer is
relatively invariant in the weak sense described above.
(Woodward 2003, p.312, emphasis and brackets added)
32

33. Do not conflate
experimental and policy interventions
Interventions to get causal knowledge vs interventions based
on available causal knowledge
Avoid gold standards
Compare comparable methods
Preserving diversity of causal methods
Observational and experimental methods
need different implementations of invariance tests
33

34. SUM UP AND CONCLUDE

35. Causal assessment
Establishing what causes what
Causal modelling and stability
Building causal structures
Role, meaning, implementation of invariance
The triad variation—regularity—invariance
Extending invariance because
it is central in causal assessment
35

36. Frisch, R. ([1938] 1995). Autonomy of economic relations. In The foundations of
econometric analysis. CUP.
Gaumé, C. and Wunsch, G. (2010). Self-rated health in the Baltic countries, 1994–1999.
European Journal of Population.
Goldthorpe, J. H. (2001). Causation, statistics, and sociology. European Sociological
Review.
Haavelmo, T. (1944). The probability approach in econometrics. Econometrica.
Illari, P. and Russo, F. (2014) Causality: Philosophical Theory Meets Scientific Practice.
OUP
Marschak, J. ([1942] 1995). Economic interdependence and statistical analysis. In The
foundations of econometric analysis. CUP
Russo, F. (2011). Correlational data, causal hypotheses, and validity. Journal for General
Philosophy of Science.
Russo, F. (2012). On empirical generalisations. In Probabilities, Laws, and Structures.
Springer.
Russo, F. (2014). What invariance is and how to test for it. International Studies in
Philosophy of Science.
Simon, H. A. (1953). Causal ordering and identifiability.
Woodward, J. (2003). Making things happen: a theory of causal explanation. OUP. 36

37. 37
Causal
assessmen
t
In
everyday
language /
situations
In the
sciences
In the
natural
sciences /
labs / …
In causal
modelling
in the
social
sciences
Model
testing
Invariance
Across
changes
Observational
Under
intervention
Experimental