3. Examples of causal claims
in economics/econometrics
Keynesian economics
Employment is a function of demand, not of supply
Keynesian policy
Government actions can change unemployment, by intervening on fiscal
deficit (e.g. tax cuts) and monetary policy (e.g. interests rates)
Friedman’s monetary theory
Price inflation and money supply are (causally) related
Phillips’ curve
The lower the unemployment in an economy, the higher the inflation
rates
…
3
4. Overview
Background
Approaches to causality
The ‘causal modelling’ tradition
Associational Models vs Causal Models
Statistical vs Causal information
Causal models
Validity and Evidential pluralism
4
7. How good are intuitions?
Exploit everyday intuitions to draw conclusions about the
metaphysics of causation from everyday or toy examples
Examples
The ‘Billy and Suzy’ saga
The assassins
…
Some conclusions
There are two concepts of cause: production and dependence
Counterfactual accounts are seriously flawed
…
7
8. Analysis of scientific practice
Growing!
CitS / PSP / PI
Philosophical questions about causation (and other topics) are motivated by
methodological and practical problems in real science
Start from scientific practice to bottom up philosophy
Partly descriptive and partly normative
Examples
Causal assessment in medicine
Causal reasoning in quantitative social science
…
Some conclusions
Causal assessment has two evidential components: mechanisms and difference-
making
‘Variation’ (rather than regularity) guides causal reasoning
…
8
9. Causal modelling has a long tradition
Staunch causalists
Quetelet, Durkheim, Wright …, Blalock, Duncan, …
Quantitative causal models make social sciences objective
Moderate skeptics
Pearl, Heckman, Hoover, …
Quantitative models do not ipso facto guarantee causality
… and the evergreen question:
When / how / under what conditions can we infer causation
from correlation?
9
11. Analysis of scientific practice
Quantitative methods
Models that establish
associations
Models that establish
causal relations
Information having
mere statistical import
Information having
causal import
11
13. H-D methodology
1. Formulate the causal hypothesis
2. Build the statistical model
3. Test the model
4. Check congruence of results with background
knowledge
Identify the research
question; conceptual
variables; indicators; …
Conditional independence
properties; invariance;
exogeneity; …
Do results make sense? Do they
feed further research? …
Specify which assumption
are statistical, extra-
statistical, or causal
13
14. Information contained
in quantitative models
Statistical
A summary of data
Inferential statistics
(sample to population)
Adequate and parsimonious
description of the
phenomenon
Statistical dependence
Causal
Opening the ‘black box’
From association to causation
Finer grained analysis of statistical
dependence; recursive
decomposition
Background ‘constraints’
Temporal priority, known causal
priority, …
Tests
Exogeneity, invariance and
stability, …
14
15. In sum
To establish causal relations
We need background knowledge
And to go beyond it
We need causal information
And to test for it
15
16. Nice but …
How much background knowledge?
Just the right amount …
What kind of causal information?
Just the relevant one …
A vicious circle introduced?
Not quite …
16
18. A question of validity
A methodological concern thoroughly analysed
since the late ‘60s
Cook & Campbell
Decide whether a model is valid
Successful inferences
Correlations, causal relations, prediction, control, …
18
19. Types of validity are about whether:
Statistical conclusion validity
The correlation (covariation) between treatment and
outcome is validly inferred
Internal
Observed covariation between treatment and outcome
reflects a causal relationship, as those variables are
manipulated or measured
Construct and External
The cause-effect relationship holds over variation in
persons, settings, treatment variables, and measurement
variables
19
20. Validity is also about evidence
Beyond the ‘Cook & Campbell Tradition’, i.e.:
Representativeness of sample and possibility to
replicate studies
Evidential pluralism
To establish causal claims, we need multiple sources
of evidence
Difference-making and mechanisms
20
21. Evidence of difference-making
Associational models
Statistical information
Dependencies
Supports the claim that E (causally) depends on C
Needs to be complemented with story about how
21
22. Evidence of mechanisms
Causal models
Causal information / hypotheses
Recursive decomposition
➣ Next, we examine this in more detail
22
23. 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
Why does this represent
a mechanism?
23
24. What mechanism?
‘Modelling mechanisms’ is not proving a ‘metaphysical
account’ of mechanism
No ontological commitment to the (degree of) physical
existence of (social) mechanisms
Mechanisms are epistemic: they carry explanatory power
‘Mechanism schemata’ give the description of the behaviour
They track something real: making sense of what actually
happens
24
25. Mechanistic explanation
Illari & Williamson:
“A mechanism for a phenomenon is composed of entities and activities
organized so that they are responsible for the phenomenon.”
Mechanistic explanation:
Identification of the phenomenon
Identification of entities and activities involved
Identification of the organisation
The recursive decomposition spells out the functioning of the mechanism
Spelling out the functioning of the mechanism means identifying the
causes, their actions, and their effect organisation
25
26. Difference-making, mechanisms
and validity
To decide whether correlations (=evidence of difference-making)
are causal we have to decide about the
validity of the whole model
That is, whether the mechanism provides
a good enough explanation of the correlations.
26
27. The causal interpretation is
model-dependent
Causal conclusions depend on the whole
‘model set up’ from which they are inferred
Statistical information + background knowledge +
causal information
Not a bad thing after all
Causation is not a ‘all or nothing’ affair
Nor a ‘once and for all’ affair
27
29. Correlation is not causation
An evergreen question
from the staunch causalists to the moderate skeptics
Analysis of scientific practice
Associational vs Causal Models
Statistical vs Causal Information
A different philosophical look at causation in
quantitative models
Validity
Evidential pluralism
29
Notes de l'éditeur
chance regularity patterns’ (Spanos, 1999)
statistical dependence.
statistical model postulates a stochastic mechanism, but does not describe it in full detail:
opens the ‘black box’
‘augmented’ statistical information: it allows additional interpretation so that an association between, say, two variables X and Y can be viewed as, for example, a causal influence from X to Y.
extra-statistical assumptions which, as we have seen, rely on different kinds of ‘background knowledge’, for instance theoretical knowledge, information about institutional mechanisms, or views on the nature of causality (e.g. temporal priority of cause) and its relations with the notion of statistical dependence.
recursive-decomposition
statistical information delivers an adequate and parsimonious description of phenomena
