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