Causality, sciences, organisational diagnosis

1. Causality in the sciences:
The conceptual toolbox for
organisational diagnosis
Federica Russo
Center Leo Apostel, Vrije Universiteit Brussel &
Centre for Reasoning, University of Kent

2. Overview
Motivations to adopt a causal stance
An approach to causation
Concepts of cause / causation
2
Objectives
Give you elements of a conceptual toolbox
It can enhance your research
It establishes links between disciplines
These links are beneficial on both sides

3. WHY ADOPTING
A CAUSAL APPROACH
3

4. Goals of causal analysis
Knowledge-oriented
Understand and explain a
phenomenon of interest
Diagnose a designed system
Action-oriented
Predict, intervene on, control
a phenomenon of interest
Design a system
4

5. Understanding, explaining, intervening
Describing vs Understanding vs Intervening
Provide:
[D] a narrative that tells what’s around
[U] a narrative that tells how what’s around is brought about
[I] indications about what to do in response to a need (then, do it)
A toolbox for explanation and intervention includes
Causes; Mechanisms; Models
5

6. You can plan ahead …
… if you know the causes / mechanisms
Demographic or economic trends
Social, economic or public health policy
The outcome of a physical theory
Interventions in a designed system
[within a certain margin, of course]
6

7. Causal assessment
‘What Causes What’
Decide what’s the cause of a patient’s illness
Decide who is (legally) responsible for some state of affairs
Decide what are the causes of a given phenomenon
Decide what causes dysfunction in an organisation
7

8. Do causes need to be causes?
Consider:
Smoking and cancer are associated. Should I quit smoking?
Smoking causes cancer. Should I quit smoking?
Causes trigger actions. Mere beliefs can’t, nor mere associations.
[What about risk factors, then? A disputed issue in medical research.]
8

9. AN APPROACH TO CAUSATION
9

10. Analysis of scientific practice
Growing!
Examples
Causal assessment in medicine
Causal reasoning in quantitative social science
Diagnosis in enterprise engineering
…
The ‘Causality in the Sciences’ research trend
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.
10

11. Theses
Causal assessment has two evidential components:
mechanisms and difference-making
‘Variation’ guides causal reasoning in model building and
model testing
Mechanisms are entities and activities organised in such a way
that they are responsible for a phenomenon
Evidence hierarchies should not neglect evidence of
mechanisms and expert knowledge
…

12. CONCEPTS OF CAUSE / CAUSATION
12

13. Difference-making:
probabilistic causality
Examples
Smoking increases the probability of developing cancer.
Physical exercise prevents heart attacks.
Cancer and yellow fingers are correlated, but both are effects of smoking.
Definitions
P(A|B) > P(A) (positive cause)
P(A|B) < P(A) (negative cause)
Principle of common cause: if A and B are correlated but are not causes
of each other, there must be a third event C that causes both
Pioneered by P. Suppes.
Still the basis of any account involving probabilities.
13

14. Difference-making:
counterfactuals
Example
Missing the train caused me to miss the class.
Had I not missed the train, I would not have missed the class.
Definition
A causes B iff, had A not been, B would not have been either.
Pioneered by D. Lewis.
Still the basis of any account involving counterfactual, including
the “potential outcome” approach in statistics
14

15. Difference-making:
manipulability theories
Example
Consider the ideal gas law, were we to manipulate the pressure of the
gas, the volume would accordingly change
Definition
A causes B iff, were we to manipulate A, B would accordingly change.
Main supporter: J. Woodward.
Widely adopted in current philosophy of science.
15

16. Physical connections:
physical processes
Example
Billiard balls colliding (causal process)
Airplane shadows crossing (pseudo-process)
Definitions
A causes B if there is a physical process connecting the two points.
The transmission of extensive quantities discriminate between a causal
and a pseudo-process
Main supporters: W. Salmon, P. Dowe.
More recently: G. Boniolo, R. Faraldo and A. Saggion
16

17. Physical connections:
mechanisms
Examples
Protein synthesis
Circadian rhythms
Definitions
A causes B iff there is mechanism linking A to B
A mechanism is an arrangement of entities and activities producing some
phenomenon
Main contemporary supporters: Machamer et al, Bechtel et al,
Glennan, …
Remote supporters: Decartes, Newton, …
17

18. Regularity
Example:
Every time I push the button the bulb lights up.
Thesis:
Causes are ‘objects’ that regularly precede their effect
in space and time.
We infer that A causes B from the observation
that B regularly follows A.
Notice:
metaphysical and epistemological reading are both possible.
Most famously: D. Hume. More recently: S. Psillos, M. Baumgartner, …
18

19. Necessary and sufficient conditions
Example:
Short circuits caused house fire. Not on its own, but in conjunction
with other factors and in a given background. It is however not
redundant because the other parts are not sufficient to cause
fire. The whole thing is itself not necessary.
Thesis:
Causes are, at minimum, INUS conditions:
“Insufficient but Non-redundant part of a condition
which is itself Unnecessary but Sufficient”
Most famously: J.L. Mackie.
Also, shared working concept of many epidemiologists.
19

20. Capacities, powers, dispositions
Example
Aspirin has the capacity to relieve headache
Definition
Causes have the capacity, power or disposition to bring about effects
Main supporter: N. Cartwright, S. Mumford, …
20

21. Causal riddles
Are omissions causes?
The gardener failed to water my plant, that subsequently died.
What entity is not watering? What process can there be from ‘not watering’
to ‘dying’?
The King didn’t water the plant either. Is he also a cause of my plant dying?
Are non-manipulable factors causes?
Gender is a cause of salary discrimination;
Ethnicity is a cause of HIV infections is sub-Saharan Africa.
But such factors cannot undergo experimental manipulation.
Are they rightly called ‘causes’?
21

22. To sum up and conclude
The philosophy of causality is becoming a discipline on its own.
‘Causality in the Sciences’ has a strong empirical and methodological basis.
Different questions to ask:
Why to adopt a causalist stance at all?
How to tackle the issue?
What does the concept amount to?
Dialogue with social, biomedical, and natural sciences has already proved
fruitful.
We work together towards the integration of Enterprise Engineering and
Causality in the Sciences.
22

23. (Highly selected!) References
• Illari P., Russo F., Williamson J. (2011). Causality in the Sciences. OUP.
• Russo F. (2009). Causality and causal modelling in the social sciences. Measuring variations. Springer.
• Williamson J. (2005). Bayesian Nets and Causality. OUP.
• Casini L., Illari P., Russo F., Williamson J. (2011). Models for predictions, explanations and control: recursive
Bayesian networks. Theoria.
• Russo F. (in press). Correlational data, causal hypotheses, and validity. Journal for General Philosophy of Science.
• Russo F. (2010). Are causal analysis and system analysis compatible approaches?, International Studies in
Philosophy of Science.
• Russo F. (2009). “Variational causal claims in epidemiology”, Perspectives in Biology and Medicine.
• Russo F. and Williamson J. (in press) Generic vs. single-case causality. The case of autopsy. European Journal for
Philosophy of Science.
• Russo F. and Williamson J. (2007). Interpreting causality in the health sciences. International Studies in
Philosophy of Science.
• Wunsch G., Russo F., Mouchart M. (2010). Do we necessarily need longitudinal data to infer causal relations?,
Bullettin de Methodologie Sociologique.
• Mouchart M., Russo F., Wunsch G. (2009). Structural modelling, exogeneity, and causality. In Engelhardt H.,
Kohler H-P, Prskwetz A. (eds). Causal Analysis in Population Studies: Concepts, Methods, Applications. Springer.
• Darby G. and Williamson J. (2011) Imaging Technology and the Philosophy of Causality. Philosophy and
Technology.
• McKay Illari and Williamson J. (2010). Function and organization: comparing the mechanisms of protein
synthesis and natural selection. Studies in History and Philosophy of Biological and Biomedical Sciences.
• Illari P. (2011). Why theories of causality need production: an information-transmission account. Philosophy and
Technology.
• Illari P. (in press). Mechanistic evidence: Disambiguating the Russo-Williamson Thesis. International Studies in
Philosophy of Science. 23

24. Analysis of ‘folk’ intuitions
Widespread
Exploit everyday intuitions to draw conclusions about
the metaphysics of causation from toy-examples
Examples
The ‘Billy and Suzy’ saga
The assassin
…
Some conclusions
There are two concepts of cause: production and dependence
Counterfactual accounts are seriously flawed
…
24

25. Analysis of causal language
Rare, but still present
Analyse the (logical) form of various types of causal claims
Examples
‘Smoking causes cancer’, All ‘Smoking causes cancer’. Versus ‘Dogs have tails’, All
‘Dogs have tails’
‘Smoking causes cancer’ versus ‘Tom’s smoking caused him cancer’
Some conclusions
There is a genuine distinction between single-case and generic causation
There is not a genuine distinction between single-case and generic causation. It’s
just a matter of quantification over single-cases.
Generic causal claims are not of the type of universally quantified claims (x …).
But what are they?
25

26. Epistemic causality
Example
“H. Pylori causes gastric ulcer” is inferred from evidence to be specified
and allows certain kinds of inferences. But it does not correspond to
anything ‘out there’
Definition
Causation is an inferential map by means of which we chart the world
Main supporter: Williamson (and some colleagues)
26