Causality is a central notion in the sciences. It is at the core of a number of epistemic practices such as explanation, prediction, or reasoning. The recognition of a plurality of practices calls, in turn, for a pluralistic approach to causality. In the ‘mosaic’ approach, as developed by Illari and Russo (2014), we need to select the causal account that best fits the practice at hand, and in the specific context. For instance, the concept of (causal) mechanism helps with explanatory practices in fields such as biology or neuroscience. Or, the concept of (causal) process helps with tracing ‘world-line’ trajectories in physics contexts or in social science. While no single notion of causality can simultaneously meet the requirements for a good explanation, prediction, or reasoning across different contexts and practices, a pluralistic approach towards the epistemology of causality seems to be the most plausible and attractive solution.
Information transmission and the mosaic of causal theory
1. Information Transmission and
the Mosaic of Causal Theory
Federica Russo
Philosophy & ILLC | University of Amsterdam
russofederica.wordpress.com |@federicarusso A longstanding
collaboration with
Phyllis Illari
2. Overview
Causality, between ‘the epistemic’ and ‘the ontic’
Central in several epistemic practices
Arguably, the ‘cement of the universe’
Causal pluralism and the causal mosaic
How to keep the epistemic and the ontic together into the same account
Information transmission
A thin metaphysics of causation that works across domains and epistemic practices
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4. The centrality of causality in epistemic
practices
Explanation
Inference and Prediction
Control
Reasoning
Arguably, in all these practices causes and causal factors figure prominently
They are not all there is about these practices, but they are certainly central and
prominent, and across different scientific domains
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6. Metaphysical questions
What is ‘causality’? What are ‘causes’? How do causes ‘produce’ their effects?
We are interested in making metaphysical claims about causation,
Cashing out the idea that causation is what ‘cement things together’, to borrow old
good Mackie
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7. What makes causal metaphysics
so hard?
Most of our causal theories are discipline-oriented:
Physics stuff causes physics stuff
Social stuff causes social stuff
Biological stuff causes biological stuff
And yet, there are plenty of cases in which we cross disciplinary boundaries, and try to
cement ‘things’ of different nature
E.g.: social factors causing biological outcomes in health and disease (and vice-versa)
What we try to ‘cement’ is often technologically constructed
Exposure research, high energy physics, agent-based models in social science, …
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8. Metaphysics and epistemology
We can easily make sense of the plurality of epistemic practices in which causality
is central:
Go for pluralism!
But how is this compatible with causal metaphysics?
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13. Types of causing
Anscombian pluralism: pulling, pushing,
binding, …
Aristotelian causes: material, formal,
efficient, final
Concepts of causation
Dependence vs production
Types of inferences
Inferential bases, inferential targets
Sources of evidence
Correlations and mechanisms
Different concepts for different scientific
fields
Physics, social science, medicine, biology
…
Methods for causal inference
Quantitative, qualitative, observational,
experimental, …
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15. 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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18. Allocating 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, and also depends on scientists’
/ philosophers’ perspectives
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19. Tiles for the
Causal Mosaic
…
necessary and sufficient;
levels; evidence;
probabilistic causality; counterfactuals;
manipulation and invariance; processes;
mechanisms; information
exogeneity; Simpson’s paradox;
dispositions;
regularity; variation;
action and agency; inference;
validity; truth;
…
Philosophical Questions
Metaphysics,
Semantics,
Epistemology,
Methodology, Use
Scientific Problems
Inference, Prediction,
Explanation, Control,
Reasoning
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23. Accounts of causal productions
Physical processes
Tied to description of processes in physics
Only one type of linking
Mechanisms
Give organization of parts and their interactions
Course-grained linking
Capacities, Powers, Dispositions
Give modal properties of some parts of mechanisms
Not about linking
23
Illari, P., Russo, F. Information
Channels and Biomarkers of
Disease. Topoi (2016)
25. The prospects of an informational
approach to causal production
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26. What is information?
An information-theoretic approach
See Philosophy of Information
Semantic information
Agreed, the most difficult to quantify
But: the most versatile, as it allows us to express informationally
virtually anything
It is related to an epistemic agent expressing informationally
something
It is related to language, but not reduced to it
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General Definition of Information:
p is an instantiation of information,
understood as semantic content, if, and
only if:
(GDI1) p consists of data;
(GDI2) data in p are well-formed;
(GDI3) well-formed data are meaningful.
(GDI4) meaningful well-formed data are
truthful.
27. The transmission of information
A development of process theory, or rather going back to the very origins:
A process is causal if it is capable of transmitting marks
Problem: counterfactual formulation
Reformulate question in (onto)epistemological terms:
We check whether a process is causal by looking at which marks are (not) transmitted
It is not formulated in terms of counterfactuals
Marks are already there
Techno-scientific practices are exactly about finding and tracing these marks
What is most important is the tracking of the transmission
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28. Why information
Gives a way of describing reality, of any kind
The transmission of information can be tracked, measured, described in quantitative or qualitative way
See e.g.: biological information
A light metaphysics, a versatile cement
In line with Anscombian pluralism
Activities and interactions is where we look for salient marks of information transmission
Helps with:
conceptualisation of link, picking up signal / signature, tracking marks
It is a form of knowledge construction
There is an ineliminable role of the epistemic agents in the process of tracking information
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30. The ATLAS experiment at the LHC
at CERN
A huge technological infrastructure for particle acceleration
Testing the Standard Model:
A bundle of theories about how matter interacts, and according to which forces
The ATLAS experiment:
Test the prediction of the Higgs boson
Test predictions beyond the Standard Model
Search for unforeseen physics processes
Analysis of data from collision events in the LHC
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• https://home.cern/science/physics/standar
d-model
• Karaca (2020), Two Senses of Experimental
Robustness: Result Robustness and
Procedure Robustness , BJP
• Karaca (2017), A case study in
experimental exploration: exploratory data
selection at the Large Hadron Collider ,
Synthese
31. A techno-scientific experiment
Technologies involved
Experimental apparatus of the LHC
Mathematical technology of Quantum Field Theory
Digital computer technology
What these technology allow us to do:
Design the experimental set up
Record all collision events
Select ‘interesting events’
31
• Karaca (2020), Two Senses of Experimental
Robustness: Result Robustness and Procedure
Robustness , BJP
• Plotnitsky A. 2016 The future (and past) of quantum
theory after the Higgs boson: a quantum-
informational viewpoint. Phil. Trans. R. Soc. A
• Stahlberg (2015),The Higgs Boson, The God Particle,
and the Correlation Between Scientific and
Religious Narratives, Open theology
32. What are ‘interesting events’?
In the processes where particle collides, we look for signature or traces of these
collisions, as predicted by SM
E.g. particle decay ‘signatures’
We never observe collisions as such, or entities colliding as such!
The causal interactions leave traces
Between quantum objects
Between quantum objects and measuring instruments
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33. How to make sense of all this?
In the midst of processes occurring, happening, intersecting we try to track, with
sophisticated instrumentation bits of information that is transferred along these
processes
What we can actually detect are interactions that leave traces and that, according
to our theory, are the right signs to look for (e.g. specific levels of decay of
particles)
[Processes, relations, interactions are ontologically prior to entities, but this is
another episode!]
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35. Attempts to find The-One-Theory of causality notoriously failed
One reason is that to account for the centrality of causality in epistemic practices and in
ontological questions may require very distinct approaches
Finding the ‘cement’ of the universe is all the more challenging because
We often cross ‘domain’ boundaries and try to establish causal relations between relata of different nature
We often construct relata and relations with sophisticated instruments and technologies
In the causal mosaic we can distinguish phil questions and sci problems, and then reconnect them
In the causal mosaic, information transmission is a thin metaphysics for causal production
We can explain what links together causal factors, even if of different nature or technologically constructed
This cement is not simply ‘out there’, but is the result of the epistemic practices performed by epistemic
agents
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36. Information Transmission and
the Mosaic of Causal Theory
Federica Russo
Philosophy & ILLC | University of Amsterdam
russofederica.wordpress.com |@federicarusso
Editor's Notes
ABSTRACT
Causality is a central notion in the sciences. It is at the core of a number of epistemic practices such as explanation, prediction, or reasoning. The recognition of a plurality of practices calls, in turn, for a pluralistic approach to causality. In the ‘mosaic’ approach, as developed by Illari and Russo (2014), we need to select the causal account that best fits the practice at hand, and in the specific context. For instance, the concept of (causal) mechanism helps with explanatory practices in fields such as biology or neuroscience. Or, the concept of (causal) process helps with tracing ‘world-line’ trajectories in physics contexts or in social science. While no single notion of causality can simultaneously meet the requirements for a good explanation, prediction, or reasoning across different contexts and practices, a pluralistic approach towards the epistemology of causality seems to be the most plausible and attractive solution.
But beyond having epistemic significance, causality is arguably the ‘cement of the universe’, to borrow the expression from the seminal work of John Mackie. What this means exactly is however made difficult in the light of the overspecialization of the sciences: is this cement the same in high energy physics and in molecular biology? Is it the same cement at the basis of social bonds and of disease onset? Two issues further complicate the picture. First, contemporary science, more often than not, crosses disciplinary boundaries, trying to establish causal relations across relata of different nature; for instance, we attempt to explain mental health conditions, such as depression, invoking biological and environmental factors, and how the two interact. Second, more often than not, contemporary science is techno-science, where instruments arguably allow for deeper and greater epistemic access to the (portion of the) world under investigation, but they do so by (partly) ‘constructing’ the object of study. For instance, the process of detection and measurement of biomarkers is not a simple and direct process giving access to a clearly identified entity, but is instead a complex process in which the ‘thing’ biomarker is much constructed via the technologies and theories employed.
How can we make sense of this complicated metaphysical picture? How can we make a metaphysics of causality compatible with an epistemology? In this talk, I explore the prospects of an informational approach to causality, as one that can offer a thin metaphysics, derived from an epistemology of techno-scientific practices. Specifically, I shall try to support the view that the mosaic view of causality, as an epistemology of causality, needs to be accompanied by a (thin) metaphysics in which causality is cashed out as information transmission. This combination, I shall argue, helps make sense of causality across different techno-scientific contexts and domains.
Floridi: constructionist, not representationalist. Knowledge is not a matter of hitting or finding, but of designing and constructing. reality is not a source but a resource for knowledge.
ABSTRACT
Causality is a central notion in the sciences. It is at the core of a number of epistemic practices such as explanation, prediction, or reasoning. The recognition of a plurality of practices calls, in turn, for a pluralistic approach to causality. In the ‘mosaic’ approach, as developed by Illari and Russo (2014), we need to select the causal account that best fits the practice at hand, and in the specific context. For instance, the concept of (causal) mechanism helps with explanatory practices in fields such as biology or neuroscience. Or, the concept of (causal) process helps with tracing ‘world-line’ trajectories in physics contexts or in social science. While no single notion of causality can simultaneously meet the requirements for a good explanation, prediction, or reasoning across different contexts and practices, a pluralistic approach towards the epistemology of causality seems to be the most plausible and attractive solution.
But beyond having epistemic significance, causality is arguably the ‘cement of the universe’, to borrow the expression from the seminal work of John Mackie. What this means exactly is however made difficult in the light of the overspecialization of the sciences: is this cement the same in high energy physics and in molecular biology? Is it the same cement at the basis of social bonds and of disease onset? Two issues further complicate the picture. First, contemporary science, more often than not, crosses disciplinary boundaries, trying to establish causal relations across relata of different nature; for instance, we attempt to explain mental health conditions, such as depression, invoking biological and environmental factors, and how the two interact. Second, more often than not, contemporary science is techno-science, where instruments arguably allow for deeper and greater epistemic access to the (portion of the) world under investigation, but they do so by (partly) ‘constructing’ the object of study. For instance, the process of detection and measurement of biomarkers is not a simple and direct process giving access to a clearly identified entity, but is instead a complex process in which the ‘thing’ biomarker is much constructed via the technologies and theories employed.
How can we make sense of this complicated metaphysical picture? How can we make a metaphysics of causality compatible with an epistemology? In this talk, I explore the prospects of an informational approach to causality, as one that can offer a thin metaphysics, derived from an epistemology of techno-scientific practices. Specifically, I shall try to support the view that the mosaic view of causality, as an epistemology of causality, needs to be accompanied by a (thin) metaphysics in which causality is cashed out as information transmission. This combination, I shall argue, helps make sense of causality across different techno-scientific contexts and domains.