Probability and Causality (original) (raw)
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A critique of Suppes' theory of probabilistic causality
Synthese, 1981
RICHARD OTTE A CRITIQUE OF SUPPES ' THEORY OF PROBABIL[STIC CAUSALITY* An analysis of causality has been particularly troublesome, and thus mostly ignored, by those who believe the world is indeterministic. Patrick Suppes has attempted to give an account of causality that would hold in both deterministic and indeterministic worlds. To do this, Suppes uses probability relations to define causal relations. The main problems facing a probabilistic theory of causality are those of distinguishing between genuine and spurious causes as well as direct and indirect causes. Suppes presents several definitions of different types of causes in an attempt to capture the distinction between genuine and spurious causes and direct and indirect causes. It is my claim that Suppes' definitions fail to distinguish among genuine and spurious causes and direct and indirect causes. To support this claim I will give some counterexamples to Suppes' theory. I will then modify some of Suppes' definitions in a natural manner, and show that even with modification they are still prone to counterexamptes. The main thrust here is that Suppes' account of causation is intrinsically defective. I believe that there is no way to differentiate genuine from spurious causes or direct from indirect causes using only probability relations; thus no minor modifications of Suppes' definitions will be sufficient to resolve these difficulties. While presenting counterexamples to Suppes' definitions, I will also try to explain in principle why each particular example is a counterexample to Suppes' theory. After presenting these counterexamples, I will introduce the idea of an interactive fork and use it to argue that the basic intuition around which Suppes built his theory is faulty. In the last section of the paper I will discuss the more fundamental issue of whether all positive causes must raise the probabilities of their effects. Although this issue lies at the heart of most probabilistic accounts of causality, it has largely been ignored in the literature. I hope to show that we are not justified in believing that positive causes always raise the probability of their effects and that more discussion is needed on this important subject.
A counterfactual analysis of causation
Mind, 1997
On David Lewis's original analysis of causation, c causes e only if c is linked to e by a chain of distinct events such that each event in the chain (counterfactually) depends on the former one. But, this requirement precludes the possibility of late pre-emptive causation, of causation by fragile events, and of indeterministic causation. Lewis proposes three different strategies for accommodating these three kinds of cases, but none of these turn out to be satisfactory. I offer a single analysis of causation that resolves these problems in one go but which respects Lewis's initial insights. One distinctive feature of my account is that it accommodates indeterministic causation without resorting to probabilities.
The British Journal for the Philosophy of Science, 2006
The paper builds on the basically Humean idea that A is a cause of B iff A and B both occur, A precedes B, and A raises the metaphysical or epistemic status of B given the obtaining circumstances. It argues that in pursuit of a theory of deterministic causation this 'status raising' is best explicated not in regularity or counterfactual terms, but in terms of ranking functions. On this basis, it constructs a rigorous theory of deterministic causation that successfully deals with cases of overdetermination and pre-emption. It finally indicates how the account's profound epistemic relativization induced by ranking theory can be undone. 1 Introduction 2 Variables, propositions, time 3 Induction first 4 Causation 5 Redundant causation 6 Objectivization 1 The major cycles have been produced by David Lewis himself. See Lewis ([1973b], [1986], [2000]). Hints to further cycles may be found there. 2 It is first presented in (Spohn [unpublished]). 3 See, e.g. the April issue of the Journal of Philosophy 97 (2000), or the collection by Collins et al. ([2004]). See also the many references therein, mostly referring to papers since 1995.
Causes, conditions and counterfactuals
Axiomathes, 2005
The article deals with one particular problem created by the counterfactual analysis of causality à la Lewis, namely the context-sensitivity problem or, as I prefer to call it, the background condition problem. It appears that Lewis’ counterfactual definition of causality cannot distinguish between proper causes and mere causal conditions – i.e. factors necessary for the effect to occur, but commonly not seen as causally efficacious. The proposal is put forward to amend the Lewis definition with a condition, based on the notion of cotenability, which would eliminate the problem. It is shown that the corrected definition of causality leads to the transitivity of the causal relation. Possible objections to the proposed solution, involving the assumption of indeterminism and the preemption cases, are given a thorough consideration.
Introduction to Special Issue of *Erkenntnis* on 'Actual Causation'
Erkenntnis, 2013
An actual cause of some token effect is itself a (distinct) token event (or fact, or state of affairs, …) that helped to bring about that effect. The notion of an actual cause is different from that of a potential cause -for example a pre-empted backup -which had the capacity to bring about the effect, but which wasn't in fact operative on the occasion in question. Sometimes actual causes are also distinguished from mere background conditions: as when we judge that the struck match was a cause of the fire, while the presence of oxygen was merely part of the relevant background against which the struck match operated. Actual causation is also to be distinguished from type causation: actual causation holds between token events in a particular, concrete scenario; type causation, by contrast, holds between event kinds in scenario kinds.
Cases and Examples in the Philosophy of Causality
This is an examination of naturalistic philosophical methodology, focusing on the causality literature. I will briefly introduce the use of examples and cases in the literature (section 2). I then turn to methodology, surveying naturalistic concerns about philosophy (section 3.1), and work on the methodology of philosophy of science (section 3). I will then show why there are legitimate concerns about the use of both toy examples and scientific cases (section 4). I move on (section 5) to examine the four influential papers I have chosen, to show how good work can still be done. I discuss what can be achieved using simplified examples (section 6), before finishing with examining in depth what scientific cases can do (section 7).
A Proposed Probabilistic Extension of the Halpern and Pearl Definition of 'Actual Cause'
2017
In their article 'Causes and Explanations: A Structural-Model Approach. Part I: Causes', Joseph Halpern and Judea Pearl draw upon structural equation models to develop an attractive analysis of 'actual cause'. Their analysis is designed for the case of deterministic causation. I show that their account can be naturally extended to provide an elegant treatment of probabilistic causation.
A Probabilistic Analysis of Causation
"ABSTRACT (Follow link above for pre-print, or below for published version) The starting point in the development of probabilistic analyses of token causation has usually been the naive intuition that, in some relevant sense, a cause raises the probability of its effect. But there are well-known examples both of non-probability-raising causation and of probability-raising non-causation. Sophisticated extant probabilistic analyses treat many such cases correctly, but only at the cost of excluding the possibilities of direct non-probability-raising causation, failures of causal transitivity, action-at-a-distance, prevention, and causation by absence and omission. I show that an examination of the structure of these problem cases suggests a different treatment, one which avoids the costs of extant probabilistic analyses. 1. Introduction 2. A Naive Probabilistic Analysis, Two Objections and a Refinement 3. Non-Probability-Raising Causation 4. Graphical Representation of Cases of Non-Probability-Raising Causation 5. Probability-Raising Non-Causation 6. Graphical Representation of Cases of Probability-Raising Non-Causation 7. Completing the Probabilistic Analysis of Causation 8. Problem Cases for Extant Probabilistic Analyses 8.1 Causation by Omission 8.2 Direct Non-Probability-Raising Causation 8.3 Failures of Transitivity 9. Conclusion"