Causality in the sciences: a gentle introduction.
1 like402 views
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.
![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.]
6
Source: http://xkcd.com/552/](https://image.slidesharecdn.com/russoparis-210226161905/85/Causality-in-the-sciences-a-gentle-introduction-6-320.jpg)
![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
…
10](https://image.slidesharecdn.com/russoparis-210226161905/85/Causality-in-the-sciences-a-gentle-introduction-10-320.jpg)
![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, …)
22](https://image.slidesharecdn.com/russoparis-210226161905/85/Causality-in-the-sciences-a-gentle-introduction-22-320.jpg)
Causality in the sciences: a gentle introduction.
- 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 2
- 3. WHY ADOPTING A CAUSAL APPROACH 3
- 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 4
- 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 … 5
- 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.] 6 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 7
- 8. THE CITS APPROACH TO CAUSATION 8
- 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. 9
- 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 … 10
- 11. TOWARDS A CAUSAL MOSAIC 11
- 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 12
- 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. 13
- 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? 14
- 15. CONCEPTS OF CAUSE / CAUSATION 15
- 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 16
- 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 17
- 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 18
- 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 19
- 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 20
- 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 21
- 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, …) 22
- 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 23
- 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 24
- 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’? 25
- 26. TO SUM UP AND CONCLUDE 26
- 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 27
- 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 28
- 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 29
- 30. Phyllis Illari and Federica Russo CAUSALITY: PHILOSOPHICAL THEORY MEETS SCIENTIFIC PRACTICE Coming soon at Oxford University Press