1. The concept of variation in causal discovery
Or, why causality needs difference
Federica Russo
Dipartimento di Studi Umanistici, Università di Ferrara
https://blogs.kent.ac.uk/federica

2. Overview
Causal reasoning and ‘variational’ epistemology
Ordinary, experimental, statistical
Foundations of variational reasoning
Mill, Durkheim
Why difference? Why (not) regularity?
Identify, explain, take action
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3. CAUSAL REASONING
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4. ‘Ordinary’ causes
“Had I left home earlier,
I wouldn’t have missed the flight”
Pin down the cause to understand
why something did (not) occur
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5. ‘Experimental’ causes
Hypothesise the function of a gene, say TP53
Knock out that gene
Observe changes in appearance, behaviour, physical &
biochemical characteristics
Reconstruct mechanisms to understand disease
causation and act in response to that knowledge
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6. ‘Statistical’ causes
Gather a large number of observations,
organise them in variables
E.g. socio-biological characteristics (exposure) and cancer
rates (disease)
Study the (in)dependencies between variables,
robustness and stability of correlations
Establish stable patterns of (in)dependencies
to identify risk factors and possible interventions
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7. EPISTEMOLOGY OF CAUSAL
REASONING
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8. Different questions, different answers
What is causation?
What are causes?
What does causality / cause mean?
How do we find out about causes?
What notions guide causal
reasoning?
What to do with causes?
How to use causal knowledge?
Metaphysics / Semantics /
Conceptual analysis
Epistemology /
Methodology
Use
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9. Concepts
Metaphysics
Methodology
Epistemolog
y
Use
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10. Different questions, different answers
What is causation?
What are causes?
What does causality / cause mean?
How do we find out about causes?
What notions guide causal
reasoning?
What to do with causes?
How to use causal knowledge?
Metaphysics / Semantics /
Conceptual analysis
Epistemology /
Methodology
Use
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11. THE RATIONALE OF VARIATION
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12. Causal discovery is reasoning about variations.
To establish causes we need difference.
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13. ‘Ordinary’ variations
“Had I left home earlier,
I wouldn’t have missed the flight”
Leaving home on time / late makes a difference to
missing the flight
Counterfactual reasoning: search for the element
changing the chain of events
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14. ‘Experimental’ variations
“Knock out TP53 and observe what happens to the
tumour’s growth”
Change putative causal factors to see
what changes (don’t) follow.
Experimental reasoning: search for those manipulable
factors changing causal structures
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15. ‘Statistical’ variations
“Gather data about socio-economic status, occupation,
diet, smoking behaviour and see how steadily they
are associated with cancer”
Study how variations in exposure are related to
variations in disease.
How different levels of exposure change the probability
of disease.
Statistical reasoning: search for those factors explaining
the variance of the outcome.
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16. FOUNDATIONS
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17. Variations in MillAgreement:
comparing different instances in which the
phenomenon occurs.
Difference:
comparing instances in which the
phenomenon does occur with similar
instances in which it does not.
Residues:
subducting from any given phenomenon all
the portions which can be assigned to
known causes, the remainder will be the
effect of the antecedents which had been
overlooked or of which the effect was as
yet an un-known quantity.
Concomitant Variation:
in presence of permanent causes or
indestructible natural agents that are
impossible either to exclude or to isolate,
we can neither hinder them from being
present nor contrive that they shall be
present alone. Comparison between
concomitant variations will enable us to
detect the causes.
Mill (1843), System of Logic
The experimental method is based
on the Baconian rule of varying
the circumstances
The Four Methods are all based on
the evaluation of variations
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18. Variations in Durkheim
Durkheim (1897), Le suicide
A study into the variability of suicide rate.
A search for the causes making suicide rate vary.
Durkheim (1885), Les règles de la méthode sociologique
The method of concomitant variations
makes sociology scientific.
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19. WHY DIFFERENCE?
Identify, explain, take action
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20. Identify (putative) causes
Day follows night, night follows day.
Days follow night regularly.
But day and night are different.
Search for the element that makes day and night
different, regularly different.
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21. Explain with causes
Gene TP53 regulates cell cycle, including tumor
suppression. People with mutations of the gene have
25% chances of developing cancer.
Causes are ‘difference-makers’ in mechanisms
regulating health and behaviour.
Explain a phenomenon by appealing to causes.
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22. Take action exploiting causes
‘5 a day’ campaign for a healthy diet
Cancer screening tests
…
We want to know causes because
we want to make things different
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23. WHY NOT REGULARITY?
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24. Learning ‘ordinary’ causes
Humean regularity
Instances of smoke follow instances of fire
Can’t establish logical, necessary link
Create expectation, project causal belief onto the future
Studies in causal cognition to tell us whether we learn
by observing regularities
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25. Learning ‘scientific’ causes
Causal discovery (experiments, statistics)
Search for differences
Explaining differences
Variation, difference, comes first
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26. Regularity too
Statistical regularity
Causal methodology needs regularity as a constraint on
variations, differences
Scientific causes are ‘generic’
Population-level, repeatable
Hence we need regularity to establish generic level
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27. SUM UP AND CONCLUDE
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28. Metaphysics /
Conceptual Analysis
• What is
causation?
• What are causes?
• What does
causality / cause
mean?
Epistemology /
Methodology
• How do we find
out about
causes?
• What notions
guide causal
reasoning?
Use
• What to do with
causes?
• How to use causal
knowledge?
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29. The rationale of variation…
… underpins causal discovery
Ordinary
Experimental
Statistical
Variation, difference
the common denominator of various forms
of causal reasoning
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30. Causal discovery is reasoning about variations.
To establish causes we need difference.
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31. Further ‘variational’ readings
Russo F. (2009). Causality and Causal Modelling in the Social Sciences.
Measuring Variations. Springer.
Russo F. (2011). Correlational data, causal hypotheses, and validity. Journal for
General Philosophy of Science, 42(1), 85-107.
Russo F. (2012). On empirical generalisations. In D. Dieks, W.J. Gonzalez, S.
Hartmann, M. Stoeltzner, M. Weber (eds), Probabilities, Laws, and
Structures, 133-150, Springer.
Russo F. (2009). Variational causal claims in epidemiology, Perspectives in
Biology and Medicine, 52(4), 540-554.
Russo F. (2006). The rationale of variation in methodological and evidential
pluralism. Philosophica, 77. Special Issue on Causal Pluralism, 97-124.
Illari P. and Russo F. Causality: Philosophical Theory Meets Scientific Practice.
Oxford University Press. Under contract.
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