Causality, pluralism

1. Scientific problems and philosophical questions
about causality:
Why we need a pluralistic approach
Federica Russo
Philosophy | Humanities | Amsterdam
russofederica.wordpress.com | @federicarusso

2. Overview
Approaches to causality
Conceptual analysis, analysis of scientific practice
Causal pluralism
A plurality of pluralism
Fragmenting and unifying causal theory
A case study
Causality in data-driven health sciences
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3. 3

4. APPROACHES TO CAUSALITY
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5. Conceptual analysis
What explicates the concept of ‘causality’
What makes causal claims true
What is causality, metaphysically
5

6. 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’ episodes
The assassins
…
Some conclusions
There are two concepts of cause: production and dependence
Counterfactual accounts are seriously flawed
…
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7. 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
Mechanisms are important for explanation
…
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8. CAUSAL PLURALISM
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9. Making sense of
a vast intellectual enterprise
Philosophical theorising about causes
Long history, ups and downs, harsh criticisms, dominant
views, etc
Expansion of philosophical theorising about causes
Beyond physics, attention to the special sciences, and
medicine
Attention for questions about use, besides traditional
metaphysics, epistemology, and semantics
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10. 10
Causal pluralism:
Causality cannot be reduced to one single concept
but has to be analysed using several concepts

11. PLURALITY OF PLURALISMS
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12. Types of causing
Anscombian pluralism: pulling, pushing, binding, …
Aristotelian causes
Concepts of causation
Hall: Dependence vs production
Types of inferences
Inferential bases, inferential targets.
Epistemic causality
Sources of evidence
Difference-making and mechanisms
Different concepts for different scientific fields
Physics, social science, …
Methods for causal inference
Quantitative, qualitative, observational, experimental, …
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13. FRAGMENTING CAUSAL THEORY
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14. 5 philosophical questions
Metaphysics
What is causality? What kind of things
are causes and effects?
Semantics
What does it mean that C causes E?
Epistemology
What notions guide causal reasoning?
How can we use C to explain E?
Methodology
How to establish whether C causes E?
Or how much of C causes E?
Use
What to do once we know that C
causes E?
5 scientific problems
Inference
Does C cause E? To what extent?
Prediction
What to expect if C does (not) cause
E?
Explanation
How does C cause or prevent E?
Control
What factors to hold fixed to study the
relation between C and E?
Reasoning
What considerations enter in
establishing whether / how / to what
extent C causes E?
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15. THE CAUSAL MOSAIC
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16. Inference, Prediction, Explanation,
Control, Reasoning
Causal Mosaic
Metaphysics, Semantics,
Epistemology, Methodology, Use
Necessary
and
sufficient Levels
Evidence
Probabilistic
causality
Counterfact
uals
Manipulatio
n
Invariance
Exogeneity
Simpson’s
Paradox
Process
Mechanism
Information
Dispositions
Regularity
Variation
Action
Inference
Validity
Truth
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17. Unifying the fragments
into the ‘causal mosaic’
A (causal) mosaic is picture made of tiles
Each fragment has a role that
Is determined by the scientific challenge /
philosophical question it addresses
Stands in a relation with neighboring concepts
The causal mosaic is dynamic, partly depends on
scientists’ / philosophers’ perspectives
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18. Accounts of causality Counterexamples
Scope Relevant questions How many Found in the literature
All possible worlds. What does causation
logically mean?
One logically possible
example.
Witches casting spells;
Angels protecting
glasses.
Worlds close to the
actual world.
What is causation
metaphysically?
One metaphysically
possible example.
World with reverse
temporal direction;
Salmon’s moving spot of
light.
This world. What is causation in this
world?
One or more real
examples.
Kinetic theory of gases /
quantum mechanics;
Billy and Suzy / bombing
the enemy town.
Some region in this
world.
What is causation in
biochemistry, or
physics?
A few real examples in
the relevant domain.
Causality in protein
synthesis mechanisms.
Some region of this
world at some time.
What kind of causal
explanation can we give
of the economic crisis in
1929? Can we give the
same kind of
explanation of the
economic crisis now?
A few real examples in
the relevant domain at
the relevant time;
Typical not skewed
examples.
Causality in the
discovery of protein
synthesis. Causality in
systems biological
approaches to protein
synthesis.
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19. EXAMPLES OF
WELL-PLACED TILES
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20. Mecha
nisms
Explanation
Control
Discovery
Use
Processes
(Salmon-
Dowe)
Metaphysics
of physical
causation
Inference
Prediction
Counterf
actuals
Semantics
Reasoning
Inference
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21. Positioning the tiles
Scientific areas that are well-studied
Physics
Biology and neuroscience
Algorithmic search and causal language
…
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22. What about new areas?
Which tiles? How to place them?

23. DATA-DRIVEN HEALTH SCIENCES
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24. A science approach
to health and disease
Discovering the mechanisms of health and disease
A tradition that starts at least in the 19th C
Experimental medicine, biology, …
Establishing correlations between categories –
disease, exposure, determinants …
A tradition that starts at least in the 17th C, blossoming
in 2nd half of 20th C
Epidemiology: the study how health and disease vary within
and across populations
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25. From epidemiology to
molecular epidemiology
Environmental exposure and disease
(How) do {air, water, chemicals, …} cause {cancer, asthma,
allergies, ...}?
Traditional epidemiology
Establish correlation between classes of environmental
factors and of disease
Molecular epidemiology
Measurements at molecular level
Identify biomarkers of exposure >> of early clinical changes
>> of disease
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26. Exposure causes Disease
Measure chemicals in water, air, etc
Identify biomarkers of exposure
Detect biomarkers of early clinical
changes
Match with biomarkers of disease 26
Make categories
of environmental
factors
Match with
categories of
disease

27. BIG HEALTH DATA AND BIG STAKES
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28. Data- and technology- driven
Big data helps with the generation of evidence
of correlation (statistical analyses) and of
mechanisms (omics analyses)
Specifically, technology is central to: generating,
processing, storing / archiving, curating,
analysing data
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29. So far, some familiar tiles
 Mechanisms, correlations
 Discovery, inference, explanation
What if change the question?
Not just ‘What is causation?’ …
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30. What are we measuring?
What is it that causes disease?

31. THE AETIOLOGICAL STANDPOINT
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32. The ‘old’ aetiological standpoint
Carter 2003, The rise of causal concepts of disease
Aetiological standpoint from Koch and Pasteur onwards
Disease is defined in terms of its causes
Causes are natural, universal, necessary
Canguilhem 1943, Le normal et le pathologique
“To act, it is at least necessary to localise”
The experimental approach of Bernard
We can localise pathogens!
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33. Koch’s aetiological standpoint 1.1
From mono-causal to multi-causal frameworks
Social determinants
…
But the idea remains: we search for those
entities that cause disease
Why? To understand and to control disease
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34. The promise of biomarkers research
Using these ‘-omics’ technologies to inform hypothesis-directed
pathway-based approaches to molecular epidemiology and to help direct
genome-wide exploratory analyses into more promising directions. [...]
one might think of the ‘‘-omics’’ data as providing the missing link
among exposure, genes, and disease. (Thomas 2006, p. 490)
‘-Omics’ tools can be directly applied to samples from an epidemiologic
case-control or cohort study to better characterise intermediate
pathways, potentially providing the ‘missing links’ among
exposures, genes, and diseases. (Vineis et al. 2009)
Omic technologies offer great potential to identify biomarkers. (Vineis
and Chadeau-Hyam 2011)
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35. The new aetiological standpoint?
Are biomarkers the sought
tiny-tiny entities?

36. THE ONTOLOGICAL STATUS OF
BIOMARKERS
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37. 37
Traditions
of
Entity
Hunting
Aetiological
standpoint /
infectious disease
model / search
for pathogens
Mechanistic
explanation /
causally relevant
entities
Analytic
metaphysics /
Language and
ontology / focus
on particulars

38. Picking up entities?
While classical statistical models to analyzing -omics data serve the
purpose of identifying signals and separating them
from noise, little has been done in chronic diseases to model
time into the exposure-biomarker-disease continuum.
[Vineis and Chadeau-Hyam 2011]
From these two parallel analyses [statistical analyses], we obtained
lists of putative markers of (i) the disease outcome, and (ii)
exposure. These were compared in a second step in order to
identify possible intersecting signals, therefore defining
potential intermediate biomarkers.
[Chadeau-Hyam et al 2011]
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We pick up signal!

39. Early theorisations of biomarkers
A must read
Molecular epidemiologist Paul Schulte (1993)
Biomarkers
Biological markers: ‘’technologically powerful
measures of biological variables’’
Indicate events in the continuum [E----D] at different
levels
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40. What do biomarkers stand for?
It is vital to understand the nature of the relationship between the
mark and the event
“Does the marker represent an event, is it an event itself, is it a
correlate of the event, or is it a predictor of the event?
The answers to these questions may affect who is sampled, how
and when they are sampled, and what confounders or effect
modifiers are considered. […] Thus, a biologic marker often
refers to the use made of a piece of biologic
information rather than to a specific type of
information.”
Schulte, 1993, p.14-15.
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41. Process-based disease causation?
Biomarkers are not causes
They are not even markers for causes, per se
Biomarkers mark salient points of a process (=
disease) to be understood in causal terms
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42. Process ontologies
Latest insights from phil biology
To say what an individual is we must look at
processes, rather than entities
Dupré, Pradeau (and Guay), Seibt, …
We can conceive as disease as a (causal) process
Sharp distinction between normal and pathological
collapses
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43. Theoretical foundations wanted!
An epistemology based on levels of abstractions
See Phil Information approach
A process ontology
See recent Phil Bio and Ontology
An informational account of causality
See recent Phil Causality
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44. TO SUM UP AND CONCLUDE
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45. What is causal theory?
Far beyond ‘what causality is’
Reconstructing technoscientific practices
Scientific problems and philosophical questions
A battery of problems and questions about causality
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46. Why pluralism
A descriptive aim
Make justice to the variety of problems and
questions in various fields
A normative aim
Forced to specify our questions and approach
Contextualise with respect to exiting accounts
Collegially working towards addressing problems
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47. Questions?
Scientific problems and philosophical questions
about causality:
Why we need a pluralistic approach
Federica Russo
Philosophy | Humanities | Amsterdam
russofederica.wordpress.com | @federicarusso