Centre for Time :: Events :: Causality, Probability and Decision Conference

University of Sydney :: Friday 21 April 2006

Venue : Timetable : Abstracts and Slides : Enquiries

Models are a principle instrument of modern science. They are built, tested, compared, and revised in the laboratory, and subsequently, introduced, applied and interpreted in an expansive literature. Throughout this talk, I will argue that models are also a valuable tool for the philosopher of science. In particular, I will discuss how the methodology of Bayesian Networks can elucidate two central problems in the philosophy of science. The first thesis I will explore is the variety-of-evidence thesis, which argues that the more varied the supporting evidence, the greater the degree of confirmation for a given hypothesis. However, when investigated using Bayesian methodology, this thesis turns out not to be sacrosanct. In fact, under certain conditions, a hypothesis receives more confirmation from evidence that is obtained from one rather than more instruments, and from evidence that confirms one rather than more testable consequences of the hypothesis. The second challenge that I will investigate is scientific theory change. This application highlights a different virtue of modeling methodology. In particular, I will argue that Bayesian modeling illustrates how two seemingly unrelated aspects of theory change, namely the (Kuhnian) stability of (normal) science and the ability of anomalies to over turn that stability and lead to theory change, are in fact united by a single underlying principle, in this case, coherence. In the end, I will argue that these two examples bring out some metatheoretical reflections regarding the following questions: What are the differences between modeling in science and modeling in philosophy? What is the scope of the modeling method in philosophy? And what does this imply for our understanding of Bayesianism?

It is held that causality is context-dependent in many crucial ways. For one thing, the level of abstraction at which causal analysis is conducted is usually determined by the context. This holds for the phenomena encountered in everyday life, as well as for those falling under the domain of science, which can be investigated in more or less detailed ways, and/or appealing to laws characterized by different ranges of application. Furthermore, the distinction between type (or population) causation and token (or singular) causation acquires a different relevance according to the context in which causal accounts occur. While in some areas, like physics, the shift from type to token is relatively unproblematic, in other contexts that relationship is much more problematic. An investigation into the use of causality made within disciplines characterized by a weaker theoretical apparatus, like econometrics and medicine, suggests that the passage from macro (or population) to micro (or individual) causal analysis raises peculiar problems. In the context of criminal trial the divergence between general and individual causal talk is even more dramatic. A further claim is made to the effect that the mechanical and manipulative views of causality are relevant to different contexts, and should therefore be seen as integrating, rather than conflicting.

Statistical Causality: Conceptions, Connexions, Confusions, Contentions
Philip Dawid (UCL)
Slides

Modern statistical approaches to causal inference are based on a variety of distinct foundations, ingredients, assumptions and methods. These involve differing conceptions of the effects of interventions, or of stable relationships across regimes; disagreement over the roles of hypothetical and counterfactual outcomes; and varying semantics and uses for algebraic, graphical and other representations. There does however seem to be fairly broad agreement that causal inference requires significant modifications and extensions to standard statistical machinery. I shall argue that this is mistaken, and that the power of existing statistical and decision-theoretic tools to address causal issues is much greater than is commonly allowed.

Evidential Decision Theory (EDT) has it that one should act in such a way as to maximize one’s expected utility, and Newcomb’s problem is standardly thought to present a decisive counterexample to it. The Evidential Theory recommends that the victim of a Newcomb-type situation take the mad course of choosing one box rather than two—even though one knows that were one to take both boxes one would get an extra thousand dollars. Some philosophers have reacted by dropping EDT on the grounds that it has this mad consequence. Others have sought to defend EDT by arguing that it doesn’t in fact have the mad consequence. The main claim of the paper is that EDT is true and that it has the consequence in question. So its main burden will be an argument that the consequence is not mad at all. On the contrary, it is quite sensible just to take one box.

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Causation, Probability and Decision