Many ways to say ‘cause’. Or, do concurrent systemsneed causality?<br />Federica Russo<br />Philosophy, Kent<br />
In this talk …<br />Different concepts of cause / causation<br />Different approaches to causation<br />Different motivati...
Disclaimer<br />This is not a reconstruction of<br />the history and philosophy of causality.<br />This is a presentation ...
Concepts of cause / causation<br />4<br />
Regularity<br />Most famously: Hume. More recently: Psillos, Baumgartner, …<br />Thesis:<br />Causes are ‘objects’ that re...
Necessary and sufficient conditions<br />Most famously: Mackie. Also, shared working conception of many epidemiologists.<b...
Intermezzo:a note on determinism and probability<br />Please distinguish:<br />(Causal) Determinism: the doctrine accordin...
Difference-making:probabilistic causality<br />Pioneered by Suppes. Still the basis of any account involving probabilities...
Difference-making:counterfactuals<br />Pioneered by D. Lewis. Still the basis of any account involving counterfactual, inc...
Difference-making:manipulability theories<br />Main supporter: Woodward. Widely (and uncritically) adopted.<br />Definitio...
Physical connections:physical processes<br />Main supporters: Salmon – Dowe. More recently: Boniolo, Faraldo and Saggion<b...
Physical connections:mechanisms<br />Main contemporary supporters: Machamer et al, Bechtel et al, Glennan, …<br />Remote s...
Capacities, powers, dispositions<br />Main supporter: Cartwright, Mumford, …<br />Definition<br />Causes have the capacity...
Epistemic causality<br />Main supporter: Williamson (and some colleagues)<br />Definition<br />Causation is an inferential...
Causal riddles<br />Are omissions causes?<br />The gardener failed to water my plant, that died.<br />What entity is not w...
Approaches to causation<br />16<br />
Analysis of ‘folk’ intuitions<br />Widespread<br />Exploit everyday intuitions to draw conclusions about<br />the metaphys...
Analysis of causal language<br />Rare, but still present<br />Analyse the (logical) form of various types of causal claims...
Analysis of scientific practice<br />Growing!<br />The ‘Causality in the Sciences’ research trend<br />Philosophical quest...
Why adopting a causal approach<br />20<br />
Goals of causal analysis<br />Knowledge-oriented<br />Understanding and explaining <br />Action-oriented<br />Predicting, ...
Understanding and explaining<br />Describing vs understanding<br />‘To know’ is to know the causes (Aristotle)<br />Arguab...
Predicting, intervening, controlling<br />If you know the causes, you can plan ahead<br />Demographic or economic trends<b...
Causal assessment<br />Decide what’s the cause of a patient’s illness<br />Decide who is (legally) responsible for some st...
Do causes need to be causes?<br />Consider:<br />Smoking and cancer are associated. Should I quit smoking?<br />Smoking ca...
To sum up<br />The philosophy of causality is a discipline on its own<br />Different angle to tackle the issue:<br />What ...
And… how do concurrent systemssay ‘cause’?<br />27<br />
A few questions for you<br />28<br />
What it is that you are after?<br />A suitable concept of cause / causation?<br />A suitable analysis of causation?<br />C...
(Highly selected!) References<br />Illari P., Russo F., Williamson J. (2011). Causality in the Sciences. OUP.<br />Russo F...
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Many ways to say cause

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Many ways to say cause

  1. 1. Many ways to say ‘cause’. Or, do concurrent systemsneed causality?<br />Federica Russo<br />Philosophy, Kent<br />
  2. 2. In this talk …<br />Different concepts of cause / causation<br />Different approaches to causation<br />Different motivations to adopt a causal stance<br />… finally … <br />what’s causation (if any) in concurrent systems?<br />2<br />
  3. 3. Disclaimer<br />This is not a reconstruction of<br />the history and philosophy of causality.<br />This is a presentation of leading concepts, approaches,<br />and motivations that populate the present-day debate.<br />Granted, many of them have deep roots in past thinking.<br />Each position is presented in its main features,<br />abstracting from any technicalities or sophistication.<br />But this is not meant to trivialise them.<br />3<br />
  4. 4. Concepts of cause / causation<br />4<br />
  5. 5. Regularity<br />Most famously: Hume. More recently: Psillos, Baumgartner, …<br />Thesis:<br />Causes are ‘objects’ that regularly precede their effect<br />in space and time.<br />We infer that A causes B from the observation<br />that B regularly follows A.<br />Example:<br />Every time I push the button the bulb lights up.<br />Notice:<br /> metaphysical and epistemological reading are both possible.<br />5<br />
  6. 6. Necessary and sufficient conditions<br />Most famously: Mackie. Also, shared working conception of many epidemiologists.<br />Thesis:<br />Causes are, at minimum, INUS conditions:<br />“Insufficient but Necessary parts of a condition<br />which is itself Unnecessary but Sufficient”<br />Example:<br />Short circuits causes 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.<br />6<br />
  7. 7. Intermezzo:a note on determinism and probability<br />Please distinguish:<br />(Causal) Determinism: the doctrine according to which any state of the universe is wholly determined by its initial conditions and the governing laws of nature<br />Predictability: the possibility to know what a future state of the universe will be given the available information about laws and initial conditions<br />Theories of probabilistic causation: causation is inherently chancy<br />Probabilistic theories of causation: causal relations are modelled with the aid of probability and statistics<br />7<br />
  8. 8. Difference-making:probabilistic causality<br />Pioneered by Suppes. Still the basis of any account involving probabilities.<br />Definitions<br />P(A|B) > P(A) (positive cause)<br />P(A|B) < P(A) (negative cause)<br />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<br />Examples<br />Smoking increases the probability of developing cancer.<br />Physical exercise prevents heart attacks.<br />Cancer and yellow fingers are correlated, but both are effects of smoking.<br />8<br />
  9. 9. Difference-making:counterfactuals<br />Pioneered by D. Lewis. Still the basis of any account involving counterfactual, including the “potential outcome” approach in statistics<br />Definition<br />A causes B iff, had A not been, B would not have been either.<br />Example<br />Missing the train caused me to miss the class.<br />Had I not missed the train, I would not have missed the class.<br />9<br />
  10. 10. Difference-making:manipulability theories<br />Main supporter: Woodward. Widely (and uncritically) adopted.<br />Definition<br />A causes B iff, were we to manipulate A, B would accordingly change.<br />Example<br />Consider the ideal gas law, were we to manipulate the pressure of the gas, the volume would accordingly change<br />10<br />
  11. 11. Physical connections:physical processes<br />Main supporters: Salmon – Dowe. More recently: Boniolo, Faraldo and Saggion<br />Definitions<br />A causes B if there is a physical process connecting the two points.<br />The transmission of extensive quantities discriminate between a causal and a pseudo-process<br />Example<br />Billiard balls colliding (causal process)<br />Airplane shadows crossing (pseudo-process)<br />11<br />
  12. 12. Physical connections:mechanisms<br />Main contemporary supporters: Machamer et al, Bechtel et al, Glennan, …<br />Remote supporters: Decartes, Newton, …<br />Definitions<br />A causes B iff there is mechanism linking A to B<br />A mechanism is an arrangements of entities and activities that produce a behaviour<br />Examples<br />Protein synthesis <br />Circadian rhythms<br />12<br />
  13. 13. Capacities, powers, dispositions<br />Main supporter: Cartwright, Mumford, …<br />Definition<br />Causes have the capacity, power or disposition to bring about effects<br />Example<br />Aspirin has the capacity to relieve headache<br />13<br />
  14. 14. Epistemic causality<br />Main supporter: Williamson (and some colleagues)<br />Definition<br />Causation is an inferential map by means of which we chart the world<br />Example<br />“H. Pylori causes gastric ulcer” is inferred from evidence to be specified and allows certain kinds of inferences. But it does not correspond to anything ‘out there’ <br />14<br />
  15. 15. Causal riddles<br />Are omissions causes?<br />The gardener failed to water my plant, that died.<br />What entity is not watering? What process can there be from ‘not watering’ to ‘dying’?<br />Our Prime Minister did water it either. Is he also a cause of my plant dying?<br />Are non-manipulable factors causes?<br />Gender is a cause of salary discrimination;<br />Ethnicity is a cause of HIV infections is sub-Saharan Africa.<br />But such factors cannot undergo experimental manipulation.<br />Are they rightly called ‘causes’?<br />15<br />
  16. 16. Approaches to causation<br />16<br />
  17. 17. Analysis of ‘folk’ intuitions<br />Widespread<br />Exploit everyday intuitions to draw conclusions about<br />the metaphysics of causation from toy-examples<br />Examples<br />The ‘Billy and Suzy’ saga<br />The assassin<br />…<br />Some conclusions<br />There are two concepts of cause: production and dependence<br />Counterfactual accounts are seriously flawed<br />…<br />17<br />
  18. 18. Analysis of causal language<br />Rare, but still present<br />Analyse the (logical) form of various types of causal claims<br />Examples<br />‘Smoking causes cancer’, All ‘Smoking causes cancer’. Versus ‘Dogs have tails’, All ‘Dogs have tails’<br />‘Smoking causes cancer’ versus ‘Tom’s smoking caused him cancer’<br />Some conclusions<br />There is a genuine distinction between single-case and generic causation<br />There is not a genuine distinction between single-case and generic causation. It’s just a matter of quantification over single-cases.<br />Generic causal claims are not of the type of universally quantified claims (x …). But what are they?<br />18<br />
  19. 19. Analysis of scientific practice<br />Growing!<br />The ‘Causality in the Sciences’ research trend<br />Philosophical questions about causation (and other topics) are motivated<br />by methodological and practical problems in real science.<br />Start from scientific practice to bottom up philosophy.<br />Examples<br />Causal assessment in medicine<br />Causal reasoning in quantitative social science<br />…<br />Some conclusions<br />Causal assessment has two evidential components: mechanisms and difference-making<br />‘Variation’ (rather than regularity) guides causal reasoning<br />…<br />19<br />
  20. 20. Why adopting a causal approach<br />20<br />
  21. 21. Goals of causal analysis<br />Knowledge-oriented<br />Understanding and explaining <br />Action-oriented<br />Predicting, intervening, controlling<br />21<br />
  22. 22. Understanding and explaining<br />Describing vs understanding<br />‘To know’ is to know the causes (Aristotle)<br />Arguably, to explain we need to invoke the causes or the mechanisms responsible for the phenomenon<br />22<br />
  23. 23. Predicting, intervening, controlling<br />If you know the causes, you can plan ahead<br />Demographic or economic trends<br />Social, economic or public health policy<br />The outcome of a physical theory<br />… hopefully, of course<br />23<br />
  24. 24. Causal assessment<br />Decide what’s the cause of a patient’s illness<br />Decide who is (legally) responsible for some state of affairs<br />Decide what are the causes of a given phenomenon <br />24<br />
  25. 25. Do causes need to be causes?<br />Consider:<br />Smoking and cancer are associated. Should I quit smoking?<br />Smoking causes cancer. Should I quit smoking?<br />Causes trigger actions. Not mere beliefs, nor mere associations.<br />What about risk factors, then?<br />25<br />
  26. 26. To sum up<br />The philosophy of causality is a discipline on its own<br />Different angle to tackle the issue:<br />What does the concept amount to?<br />How to tackle the issue?<br />Why to adopt a causalist stance at all?<br />26<br />
  27. 27. And… how do concurrent systemssay ‘cause’?<br />27<br />
  28. 28. A few questions for you<br />28<br />
  29. 29. What it is that you are after?<br />A suitable concept of cause / causation?<br />A suitable analysis of causation?<br />Confirmatory?<br />Exploratory?<br />Bug hunting?<br />29<br />
  30. 30. (Highly selected!) References<br />Illari P., Russo F., Williamson J. (2011). Causality in the Sciences. OUP.<br />Russo F. (2009). Causality and causal modelling in the social sciences. Measuring variations. Springer.<br />Williamson J. (2005). Bayesian Nets and Causality. OUP.<br />Casini L., Illari P., Russo F., Williamson J. (2011). Models for predictions, explanations and control: recursive Bayesian networks. Theoria.<br />Russo F. (in press). Correlational data, causal hypotheses, and validity. Journal for General Philosophy of Science.<br />Russo F. (2010). Are causal analysis and system analysis compatible approaches?, International Studies in Philosophy of Science.<br />Russo F. (2009). “Variational causal claims in epidemiology”, Perspectives in Biology and Medicine.<br />Russo F. and Williamson J. (in press) Generic vs. single-case causality. The case of autopsy. European Journal for Philosophy of Science.<br />Russo F. and Williamson J. (2007). Interpreting causality in the health sciences. International Studies in Philosophy of Science.<br />Wunsch G., Russo F., Mouchart M. (2010). Do we necessarily need longitudinal data to infer causal relations?, Bullettin de MethodologieSociologique.<br />Mouchart M., Russo F., Wunsch G. (2009). Structural modelling, exogeneity, and causality. In Engelhardt H., Kohler H-P, Prskwetz A. (eds). Causal Analysis in Population Studies: Concepts, Methods, Applications. Springer.<br />Darby G. and Williamson J. (2011)Imaging Technology and the Philosophy of Causality. Philosophy and Technology.<br />McKay Illari and Williamson J. (2010). Function and organization: comparing the mechanisms of protein synthesis and natural selection. Studies in History and Philosophy of Biological and Biomedical Sciences.<br />Illari P. (2011). Why theories of causality need production: an information-transmission account. Philosophy and Technology.<br />Illari P. (in press). Mechanistic evidence: Disambiguating the Russo-Williamson Thesis. International Studies in Philosophy of Science.<br />30<br />

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