Causality in the Sciences: a gentle introduction discusses adopting a causal approach in science. It argues that causal analysis has knowledge and action oriented goals like understanding phenomena, predicting outcomes, and designing systems. The CitS (Causality in the Sciences) approach analyzes scientific practice to develop philosophical theories grounded in real world problems. It advocates a "causal mosaic" integrating multiple causal concepts like difference-making, mechanisms, capacities and regularities to match diverse scientific tasks. The document concludes causal notions should complement rather than replace scientific work, with resources like conferences and books further developing the CitS approach.

1. Causality in the Sciences:
a gentle introduction
Federica Russo
Center Leo Apostel, Vrije Universiteit Brussel &
Centre for Reasoning, University of Kent

2. Overview
Motivations to adopt a causal stance
The CitS spproach to causation
Towards a mosaic of causal theory
Accounts of cause / causation
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3. WHY ADOPTING
A CAUSAL APPROACH
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4. Goals of causal analysis
Knowledge-oriented
Understand and explain a
phenomenon of interest
Diagnose dysfunction a
designed system
Action-oriented
Predict, intervene on, control
a phenomenon of interest
Design / model / debug a
system
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5. Causal assessment
‘What Causes What’
Establish
what’s the cause of a patient’s illness
who is (legally / morally) responsible for some state of affairs
what are the causes of a given phenomenon
what causes dysfunction in an organisation
which pathways explain some cellular behaviour
…
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6. 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 debated issue in medical research.]
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Source: http://xkcd.com/552/

7. Causes make things change
Demographic or economic trends
Social, economic or public health policy
The outcome of a physical theory
Interventions in a designed system
Interventions in a lab experiment
When we know the causes / mechanisms,
we can plan to make things change
within a certain margin, of course
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8. THE CITS APPROACH TO CAUSATION
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9. 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.
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10. Examples of CitS theses
[Medicine]
Causal assessment has two evidential components:
mechanisms and difference-making
[Social Science]
‘Variation’ guides causal reasoning in model building and model testing
[Biology, Neuroscience]
Mechanisms are entities and activities organised in such a way that
they are responsible for a phenomenon
[Evidence-based approaches]
Evidence hierarchies should not neglect evidence of
mechanisms and expert knowledge
…
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11. TOWARDS A CAUSAL MOSAIC
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12. Biomarkers research
Environmental exposure and disease
Measurement at molecular level
Identify biomarkers of exposure, of early clinical changes, of disease
From GWAS to EWAS
Better understanding of disease mechanisms
Better prediction and health policy planning
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13. The ‘biomarkers’ mosaic
Meeting in the middle methodology
Relation between background knowledge and new causal discoveries.
Exposome
New (causal) concept; redefines the causal context of causal relations at different
levels take place.
Processes
Studying the evolution of biomarkers is tracing processes of molecular changes
Difference-making and mechanisms
Their interplay is crucial to establish causal relations:
Biomarkers of disease make a difference in the probability of disease
This probability raising is substantiated by a plausible mechanism.
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14. The biomarkers mosaic (cont’ed)
Production-Information
A test case for an account of production in terms of information
Levels of causation
EWAS for the population, what about the individual?
Integration of factors of different nature (social and biological) into the
same explanatory framework.
Capacity
A test case for capacities: is the predictive power of a biomarker due to a
capacity of some chemicals to induce molecular changes?
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15. CONCEPTS OF CAUSE / CAUSATION
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16. 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.
Core ideas
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.
Helps with, e.g.:
Causal inference in medical datasets analysed with temporal probabilistic logic
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17. Difference-making:
counterfactuals
Example
Missing the train caused me to miss the seminar.
Had I not missed the train, I would not have missed the seminar.
Core idea
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
Helps with, e.g.:
Heuristic search for causal hypotheses to test
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18. Difference-making:
manipulability theories
Example
Consider the ideal gas law, were we to manipulate the pressure of the gas, the
volume would accordingly change
Core idea
A causes B iff, were we to manipulate A, B would accordingly change.
Main supporter: J. Woodward. Widely adopted in current philosophy of science.
Helps with, e.g.:
Explanation of structure of lab experiments
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19. Physical connections:
physical processes
Example
Billiard balls colliding (causal process)
Airplane shadows crossing (pseudo-process)
Core idea
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
Helps with, e.g.:
Causal inference in physics contexts
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20. Physical connections:
mechanisms
Examples
Protein synthesis
Circadian rhythms
Core ideas
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, …
Helps with, e.g.:
Conceptualising interactions in complex systems
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21. Regularity
Example:
Every time I push the button the bulb lights up.
Core ideas
Causes are events that regularly precede their effect
in space and time.
We infer that A causes B from the observation
that B regularly follows A.
Most famously: D. Hume. More recently: S. Psillos, M. Baumgartner, …
Helps with, e.g.:
Causal inference of generic causal claims
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22. Necessary and sufficient components
Example:
Short circuit 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. [Analogous: gene knockout experiments]
Core ideas
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. In epidemiology: Rothman’s pie charts.
Helps with, e.g.:
Conceptualisation of complex causal settings (disease, concurrent systems, …)
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23. Capacities, powers, dispositions
Example
Aspirin has the capacity to relieve headache
Cells have the capacity to metabolise lactose
Core idea
Entities or systems have the capacity, power or disposition to bring about effects
Main supporter: N. Cartwright, S. Mumford, A. Bird, A. Chakravartti. Remote
supporter: an Aristotelian concept!
Helps with, e.g.:
Explain properties of biological systems
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24. Inferentialism
Example
“H. Pylori causes gastric ulcer” is inferred from multifaceted evidence; it allows
certain kinds of inferences. But it does not correspond to anything ‘out there’
Core idea
Causation is an inferential map by means of which we chart the world
Main supporter: J. Williamson; J. Reiss
Helps with, e.g.:
Account for inferential practices in science
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25. Some 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’?
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26. TO SUM UP AND CONCLUDE
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27. Why going ‘CitS’
The philosophy of causality is becoming a full-blown discipline.
‘Causality in the Sciences’ has a strong empirical and
methodological basis.
Whence its usefulness and importance for science
Time for a reassessment of the numerous causal theories
Abandon the causal theory
Embrace the CAUSAL MOSAIC
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28. The tiles in the mosaic
Causal notions are placed in the causal mosaic according to their role
Conceptual / metaphysical
Epistemological
Methodological
Use causal accounts to complement scientific tasks
Inference
Explanation
Prediction / control
Conceptualisation
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29. Useful resources
Causality in the Sciences Conference Series
http://www.kent.ac.uk/secl/philosophy/jw/cits.htm
Stay tuned for Paris conference in July 2013
Causality in the Sciences, OUP 2011 (Illari, Russo, Williamson eds)
Research papers on causality from the sciences2929
The Oxford Handbook of Causation, OUP 2010 (Beebee, Hitchcock, Menzies eds)
Introduction to ‘traditional’ causal theories
Judea Pearl’s causality blog
http://www.mii.ucla.edu/causality/
A lot on Bayesian nets approach
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30. Phyllis Illari and Federica Russo
CAUSALITY:
PHILOSOPHICAL THEORY MEETS SCIENTIFIC PRACTICE
Coming soon at
Oxford University Press