Many ways to say cause

Many ways to say ‘cause’. Or, do concurrent systemsneed causality?Federica RussoPhilosophy, Kent

In this talk …Different concepts of cause / causationDifferent approaches to causationDifferent motivations to adopt a causal stance… finally … what’s causation (if any) in concurrent systems?2

DisclaimerThis is not a reconstruction ofthe 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

Concepts of cause / causation4

RegularityMost famously: Hume. More recently: Psillos, Baumgartner, …Thesis:Causes are ‘objects’ that regularly precede their effectin space and time.We infer that A causes B from the observationthat B regularly follows A.Example:Every time I push the button the bulb lights up.Notice: metaphysical and epistemological reading are both possible.5

Necessary and sufficient conditionsMost famously: Mackie. Also, shared working conception of many epidemiologists.Thesis:Causes are, at minimum, INUS conditions:“Insufficient but Necessary parts of a conditionwhich 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

Intermezzo:a note on determinism and probabilityPlease 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 naturePredictability: the possibility to know what a future state of the universe will be given the available information about laws and initial conditionsTheories of probabilistic causation: causation is inherently chancyProbabilistic theories of causation: causal relations are modelled with the aid of probability and statistics7

Difference-making:probabilistic causalityPioneered by Suppes. Still the basis of any account involving probabilities.DefinitionsP(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 bothExamplesSmoking increases the probability of developing cancer.Physical exercise prevents heart attacks.Cancer and yellow fingers are correlated, but both are effects of smoking.8

Difference-making:counterfactualsPioneered by D. Lewis. Still the basis of any account involving counterfactual, including the “potential outcome” approach in statisticsDefinitionA causes B iff, had A not been, B would not have been either.ExampleMissing the train caused me to miss the class.Had I not missed the train, I would not have missed the class.9

Difference-making:manipulability theoriesMain supporter: Woodward. Widely (and uncritically) adopted.DefinitionA causes B iff, were we to manipulate A, B would accordingly change.ExampleConsider the ideal gas law, were we to manipulate the pressure of the gas, the volume would accordingly change10

Physical connections:physical processesMain supporters: Salmon – Dowe. More recently: Boniolo, Faraldo and SaggionDefinitionsA causes B if there is a physical process connecting the two points.The transmission of extensive quantities discriminate between a causal and a pseudo-processExampleBilliard balls colliding (causal process)Airplane shadows crossing (pseudo-process)11

Physical connections:mechanismsMain contemporary supporters: Machamer et al, Bechtel et al, Glennan, …Remote supporters: Decartes, Newton, …DefinitionsA causes B iff there is mechanism linking A to BA mechanism is an arrangements of entities and activities that produce a behaviourExamplesProtein synthesis Circadian rhythms12

Capacities, powers, dispositionsMain supporter: Cartwright, Mumford, …DefinitionCauses have the capacity, power or disposition to bring about effectsExampleAspirin has the capacity to relieve headache13

Epistemic causalityMain supporter: Williamson (and some colleagues)DefinitionCausation is an inferential map by means of which we chart the worldExample“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

Causal riddlesAre 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

Analysis of ‘folk’ intuitionsWidespreadExploit everyday intuitions to draw conclusions aboutthe metaphysics of causation from toy-examplesExamplesThe ‘Billy and Suzy’ sagaThe assassin…Some conclusionsThere are two concepts of cause: production and dependenceCounterfactual accounts are seriously flawed…17

Analysis of causal languageRare, but still presentAnalyse the (logical) form of various types of causal claimsExamples‘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 conclusionsThere is a genuine distinction between single-case and generic causationThere 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

Analysis of scientific practiceGrowing!The ‘Causality in the Sciences’ research trendPhilosophical questions about causation (and other topics) are motivatedby methodological and practical problems in real science.Start from scientific practice to bottom up philosophy.ExamplesCausal assessment in medicineCausal reasoning in quantitative social science…Some conclusionsCausal assessment has two evidential components: mechanisms and difference-making‘Variation’ (rather than regularity) guides causal reasoning…19

Why adopting a causal approach20

Goals of causal analysisKnowledge-orientedUnderstanding and explaining Action-orientedPredicting, intervening, controlling21

Understanding and explainingDescribing 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 phenomenon22

Predicting, intervening, controllingIf you know the causes, you can plan aheadDemographic or economic trendsSocial, economic or public health policyThe outcome of a physical theory… hopefully, of course23

Causal assessmentDecide what’s the cause of a patient’s illnessDecide who is (legally) responsible for some state of affairsDecide what are the causes of a given phenomenon 24

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

To sum upThe philosophy of causality is a discipline on its ownDifferent 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

And… how do concurrent systemssay ‘cause’?27

What it is that you are after?A suitable concept of cause / causation?A suitable analysis of causation?Confirmatory?Exploratory?Bug hunting?29

(Highly selected!) ReferencesIllari 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

Many ways to say cause

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