This document discusses some of the conceptual challenges involved in big data practices, particularly regarding causality. It begins by questioning what causation means in the context of big data, where correlations are often taken as causal without a theoretical understanding. It then provides a crash course on traditional and pluralist philosophical views of causality. Several key questions for analyzing causality are identified, as well as levels of analysis. The document argues that conceptual challenges of causality have not changed with big data and advocates adopting a strategy like Bradford Hill's to make viewpoints on causality explicit based on data and other evidence, without treating them as rigid criteria. It suggests big data can help identify causes while resisting scientism and skewing of science and technocracy

1. Conceptual challenges
in big data practices
Federica Russo
Philosophy & ILLC | Amsterdam
russofederica.wordpress.com |
@federicarusso

2. Are there causes in big data?
How to identify them?
The homework that was assigned to
me

3. (big data) practices
Investigation into scientific practices
A general approach, on the rise in philosophy of science
Philosophical issues as they arise in the practice of
science, rather than in theory

4. Conceptual challenges
Not about practical aspects of research
But about the foundations
What do we want from big data?
The promise
‘true correlations’, theory not needed anymore
‘true correlations’, enough for prediction
‘true correlations’ = causation?
What’s causation, then?!

5. A crash course on
traditional philosophy of
causality

6. Fill in your favourite …
X causes Y if, and only if
…
The probability raises
such and such …
There is a mechanism
…
There is a continuous
process …
X has the power to …
Bad news: all these strategies fail.
No way we get to understand what causality is.

7. A crash course on
causal pluralism
Causality cannot be reduced to one single
concept
but has to be analysed using many concepts

8. How to choose?
First, pick your question.
Then, pick your concept(s).

9. 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?

10. Inference, Prediction, Explanation,
Control, Reasoning
Causal Mosaic
Metaphysics, Semantics,
Epistemology, Methodology, Use
Necessary
and
sufficient Levels
Evidence
Probabilistic
causality
Counterfact
uals
Manipulation
Invariance
Exogeneity
Simpson’s
Paradox
Process
Mechanism
Information
Dispositions
Regularity
Variation
Action
Inference
Validity
Truth

11. Conceptual challenge
has not changed
in big data
What is causality??

12. Conceptual challenges
in big data practices
opportunities

13. A “Bradford Hill strategy”
We can claim that X causes Y when
Data
Technological equipment
Algorithm design
Background theory / knowledge
Previous studies
Measured / constructed variables
Known / hypothesised mechanisms
is/are
such and such
Viewpoints!
Not criteria

14. What opportunity is it?!
Emphasise the role of the modeller / scientist /
epidemiologist
Understand causality primarily as an epistemic
construct, based on evidence, data etc.
Account for validity of causal results also in non-
experimental contexts
Allow for revision/update of results

15. Any pay-off?
Make viewpoints explicit without turning
them into a rigid check-list
We find causes in big data
We resist scientism
We skew technocracy
We defend science

16. Conceptual opportunities
in big data practices
Federica Russo
Philosophy & ILLC | Amsterdam
russofederica.wordpress.com |
@federicarusso
Thank you!