It is now common to hold that causes do not always (and perhaps never) determine their
effects, and indeed theories of "probabilistic causation" abound. The basic idea of these
theories is that C causes E just in case C and E both occur, and the chance of E would have
been lower than it is had C not occurred. The problems with these accounts are that (i) the
notion of chance remains primitive, and (ii) this account of causation does not coincide with the
intuitive notion of causation as ontological support.
Turning things around, I offer an analysis of chance in terms of causation, called the
causal theory of chance. The chance of an event E is the degree to which it is determined by its
causes. Thus chance events have full causal pedigrees, just like determined events; they are not
"events from nowhere". I hold that, for stochastic as well as for deterministic processes, the
actual history of a system is caused by its dynamical properties (represented by the lagrangian)
and the boundary condition. A system is stochastic if (a description of) the actual history is not
fully determined by maximal knowledge of these causes, i.e. it is not logically entailed by them.
If chance involves partial determination, and determination is logical entailment, then
there must be such a thing as partial entailment, or logical probability. To make the notion of
logical probability plausible, in the face of current opposition to it, I offer a new account of
logical probability which meets objections levelled at the previous accounts of Keynes and
The causal theory of chance, unlike its competitors, satisfies all of the following criteria:
(i) Chance is defined for single events.
(ii) Chance supervenes on the physical properties of the system in question.
(iii) Chance is a probability function, i.e. a normalised measure.
(iv) Knowledge of the chance of an event warrants a numerically equal degree of belief, i.e.
Miller's Principle can be derived within the theory.
(v) Chance is empirically accessible, within any given range of error, by measuring relative
(vi) With an additional assumption, the theory entails Reichenbach's Common Cause Principle
(vii) The theory enables us to make sense of probabilities in quantum mechanics.
The assumption used to prove the CCP is that the state of a system represents complete
information, so that the state of a composite system "factorises" into a logical conjunction of
states for the sub-systems. To make sense of quantum mechanics, particularly the EPR
experiment, we drop this assumption. In this case, the EPR criterion of reality is false. It states
that if an event E is predictable, and locally caused, then it is locally predictable. This fails
when maximal information about a pair of systems does not factorise, leading to a non-locality
of knowledge. / Arts, Faculty of / Philosophy, Department of / Graduate
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