Kent Comp Dep May06


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  • [attention getter] [need] If we want philosophy to really matter for the sciences – i.e. for their practice and methodology – we (the philosophers) have to change our perspective, our methodology of research, increase interchanges with scientists. [task] Using what I call a bottom-up methodology and facing direct confrontation with scientists has been one of the goals of the PhD and I wish to persist in this program in postdoc research [main message] The sciences raise crucial conceptual i.e. philosophical problems. These problems, although “philosophical” in character are of direct interest for the scientist’s methodology and practice. Causality and probability are philosophical problems IN the sciences
  • [browse on topics]
  • This slide is about philosophy, in particular about two different ways of doing philosophy. The question “what is philosophy” (  metaphilosophy) is a complicated one … vast debate. Take it as my personal views – don’t pretend to be representative of any school of thought. Take it as the questions I ask myself in doing philosophical research – what for? Is it useful? Why? Etc. Traditionally, my discipline is called philosophy OF science. I want to argue that we need philosophy IN science. *OF* something suggests a detachment, *IN* something suggests a real involvement.  in this sense I talk about pure vs applied, practically oriented In sum, philosophy has – or ought to have – a *real* bearing on methodology and practice of the sciences The two methodologies: top-down vs bottom-up
  • I’m not an historian, so won’t do history of philosophy. Just to point out that we are not reinventing the wheel, causality and probability have long tradition in philosophical thought. Once we recon this long history behind, the question is “what more can we tell?”. Answer: a lot. (i) because we have to recontextualize those problems in *current* scientific practice and methodology, (ii) because we are “dwarfs on the shoulders of giants” (Bernard of Chartres, mistakenly attributed to Newton – check Wiki!) Causality. I like the way Federico Laudisa (Italian philosopher of science) put the history of causality in terms of “pattern of knowledge”. Since ancient Greece (Aristotelian 4 cause), through Middle Age to Humean and the contemporary debate, causality as a principle that allows us to *know* about the world. Some push further in say that causality itself is a feature (an “element”) of reality (  realists). Contemporary debate heritage of Hume (regularity view), important step: attempts to dismiss causality (Russell in philosophy, Mach in physics, Pearson in statistics); advent of modern science requires also probabilistic account (because of possibility of indeterminism?). A very interesting problem is the connection btw causality and probability – shall get back later. Probability. If we mean probability in the sense of “possibility” thus tied to modality, surely it is as old as causality, with very interesting developments of modal logic already in the Middle Age. But strictly speaking probability comes into range with correspondence Pascal-Fermat trying to find solutions for games of chance (all this is very well known). Well known also that we had to wait Kolmogorov in 1933 for an axiomatization. BUT one problem is the formalization (axioms, theorems, inference rules) another is the interpretation. On the first there is a core of agreement, the second is a battle field. Numerous interpretations, isolate 4 main braches: logical/classical, physical, subjective, objective Bayesian. Causality-Probability. The advent of probabilistic modelling (in various ways in various disciplines) raises the problem of connection with causality (provided that a causalist stance is adopted). Is causality probabilistic in the sense that causes *are* probabilistic – ie there is a genuine chancy element that prevent deterministic causal relations? Is causality deterministic but our *knowledge* is imperfect and thus probabilistic? Q: what more can be said in 2006? Wrong Q. Q: What problems do contemporary sciences arise with respect to causality and probability? Next, I’ll give 2 examples. (i) the main thesis of my phd, (ii) joint work with Jon within the project at SECL.
  • Give the context. Causal modelling in the social sciences. What sciences. What kind of modelling. What kind of case studies. Explain the research question. The rationale  epistemological question about the guiding causal principles in causal modelling. (Epistemological question  how do we know) NOT metaphysical  what is causality ; NOT methodology  what methods Received view. Regularity. Hume, probabilistic regularity, invariance condition as a form of regularity. (some go even further: regularity as metaphysics= all there is about causality is regularity) Breaking the received view: VARIATION. explain the rationale in probabilistic theories, in structural equations. Stress the different starting point with respect to regularity and invariance (conceptual precedence).
  • Why cancer epidemiology? Project on causality and interpretation of probability in social and health sciences. Had a look at the literature we got interested in cancer epidemiology for 2 reasons. (i) epidemiology seems paradigmatic of social and health sciences (it integrates social and biological factors) (ii) use of probabilistic models where 2 types are performed. Types of inference. Population-level – Individual-level. Their features, both probabilistic, Q: is meaning of probability the same? There isn’t much discussion about it among scientists. Philosophers don’t really talk about specific domains … Empirically-based or objective Bayesianism. what is it? why?  fits the interpretation at the two levels. Meaningful interpretation for cognitive goal (know about causal relations) and for practical goal (making decisions)
  • [to sum up] In spite of long history of those concepts in philosophy, causality and probability are still appealing, fashionable and worth exploring. What makes them so “present-day” is a different perspective or methodology of research in philosophy: bottom-up. Start from scientific methodology and practice and raise philosophical problems and give philosophical arguments. Recall the two examples: (i) variation in causal modelling, (ii) objective Bayesian probabilities in cancer epidemiology
  • [say a few words on those topics] Concept of cause in cancer epidemiology. Full account of levels of causation Explanation in causal modelling Much more .. Also depending on collaboration with scientists!
  • Kent Comp Dep May06

    1. 1. Causality and Probability in the Sciences Federica Russo Philosophy, University of Kent
    2. 2. Overview <ul><li>Top-down vs. bottom-up philosophy </li></ul><ul><li>Causality and Probability: </li></ul><ul><li>evergreen philosophical problems </li></ul><ul><li>Epistemology and methodology </li></ul><ul><li>of causal modelling </li></ul><ul><li>Interpreting probability </li></ul><ul><li>in cancer epidemiology </li></ul>
    3. 3. Top-down vs. bottom-up philosophy <ul><li>Philosophical problems in the sciences: what does it mean? </li></ul><ul><li>Pure vs. applied philosophy </li></ul><ul><li>Practically-oriented philosophy </li></ul>
    4. 4. Evergreen philosophical problems <ul><li>… we are dwarfs on the shoulders of giants … </li></ul><ul><li>The long history of causality </li></ul><ul><li>The shorter but intense history of probability </li></ul>
    5. 5. Epistemology and methodology of causal modelling <ul><li>Causal modelling in the social sciences: </li></ul><ul><li>What’s the rationale ? </li></ul><ul><li>The received view: a rationale of regularity </li></ul><ul><li>My proposal: a rationale of variation </li></ul>
    6. 6. Interpreting probability in cancer epidemiology <ul><li>(joint work with Jon Williamson) </li></ul><ul><li>Cancer epidemiology </li></ul><ul><li>and types of inferences </li></ul><ul><li>The received view … </li></ul><ul><li>to be received yet! </li></ul><ul><li>Our proposal: </li></ul><ul><li>objective Bayesian probabilities </li></ul>
    7. 7. To sum up… <ul><li>Causality and probability are </li></ul><ul><li>fashionable problems still in 2006 </li></ul><ul><li>The bottom-up methodology makes them </li></ul><ul><li>philosophical problems IN the sciences </li></ul><ul><li>Two examples of bottom-up research </li></ul><ul><li>in philosophy: </li></ul><ul><ul><li>variation in causal modelling </li></ul></ul><ul><ul><li>objective Bayesian probabilities </li></ul></ul><ul><ul><li>in cancer epidemiology </li></ul></ul>
    8. 8. What next … <ul><li>Concept of cause in cancer epidemiology, </li></ul><ul><li>full account of levels of causation, </li></ul><ul><li>explanation in causal modelling, … </li></ul><ul><li>And much more … </li></ul>