According to a causal model of explanation, we explain phenomena by giving their causes or, where the phenomena are themselves causal regularities, we explain them by giving a mechanism linking cause and effect. If we explain why smoking causes cancer, we do not give the cause of this causal connection, but we do give the causal mechanism that makes it. The claim that to explain is to give a cause is not only natural and plausible, but it also avoids many (...) of the objections to other accounts of explanation, such as the views that to explain is to give a reason to believe the phenomenon occurred, to somehow make the phenomenon familiar, or to give a Deductive-Nomological argument. Unlike the reason for belief account, a causal model makes a clear distinction between understanding why a phenomenon occurs and merely knowing that it does, and the model does so in a way that makes understanding unmysterious and objective. Understanding is not some sort of super-knowledge, but simply more knowledge: knowledge of the phenomenon and knowledge of its causal history. A causal model makes it clear how something can explain without itself being explained, and so avoids the regress of whys, since we can know a phenomenon's cause without knowing the cause of the cause. It also accounts for legitimate self-evidencing explanations, explanations where the phenomenon is an essential part of the evidence that the explanation is correct, so the explanation can not supply a non-circular reason for believing the phenomenon occurred. There is no barrier to knowing a cause through its effects and also knowing that it is their cause. The speed of recession of a star explains its observed red-shift, even though the shift is an essential part of the evidence for its speed of recession. The model also avoids the most serious objection to the familiarity view, which is that some phenomena are familiar yet not understood, since a phenomenon can be perfectly familiar, such as the blueness of the sky or the fact that the same side of the moon always faces the earth, even if we do not know its cause. Finally, a causal model avoids many of the objections to the Deductive-Nomological model. Ordinary explanations do not have to meet the requirements of the Deductive-Nomological model, because one does not need to give a law to give a cause, and one does not need to know a law to have good reason to believe that a cause is a cause. As for the notorious over-permissiveness of the Deductive-Nomological model, the reason recession explains red-shift but not conversely, is simply that causes explain effects but not conversely, and the reason a conjunction of laws does not explain its conjuncts is that conjunctions do not cause their conjuncts. (shrink)

Causation is widely assumed to be a binary relation: c causes e. I will argue that causation is a quaternary, contrastive relation: c rather than C* causes e rather than E*, where C* and E* are nonempty sets of contrast events. Or at least, I will argue that treating causation as contrastive helps resolve some paradoxes.

There is, no doubt, a temptation to treat preventions, such as ‘the father’s grabbing the child prevented the accident’, and cases of ‘causation’ by omission, such as ‘the father’s inattention was the cause of the child’s accident’, as cases of genuine causation. I think they are not, and in this paper I defend a theory of what they are. More specifically, the counterfactual theory defended here is that a claim about prevention or ‘causation’ by omission should be understood not as (...) being directly about actual genuine causation but primarily as a counterfactual claim about genuine causation.1 The relation between actual causation and the mere possibility of causation allows my theory to explain both the difference and the similarity between the two notions (causation and prevention/omission). Further, the difference explains certain intuitions we have and the similarity justifies and explains the fact that for practical purposes we usually treat preventions and omissions as if they were genuine causation. Finally, the fact that this counterfactual theory of prevention and omission takes causation as primitive suggests that it is consistent with any theory of causation. This allows us to construct two arguments against what I will call genuinism, the view that cases of prevention and ‘causation’ by omission really are cases of genuine causation. In section II I show that genuinism does not account for the so-called intuition of difference. In section III I outline a number of problems that various theories of causation have with preventions and ‘causation’ by omission. These problems are ipso facto problems for genuinism, whereas I show in section V how the counterfactual theory solves those problems. Further, I answer a genuinist argument based on another type of intuition—the genuinist intuition—by showing why we have that intuition and how it should be handled (section VI). (shrink)

The thesis of underdetermination presents a major obstacle to the epistemological claims of scientific realism. That thesis is regularly assumed in the philosophy of science, but is puzzlingly at odds with the actual history of science, in which empirically adequate theories are thin on the ground. We propose to advance a case for scientific realism which concentrates on the process of scientific reasoning rather than its theoretical products. Developing an account of causal–explanatory inference will make it easier to resist the (...) thesis of underdetermination. For, if we are not restricted to inference to the best explanation only at the level of major theories, we will be able to acknowledge that there is a structure in data sets which imposes serious constraints on possible theoretical alternatives. We describe how Differential Inference, a form of inference based on contrastive explanation, can be used in order to generate causal hypotheses. We then go on to consider how experimental manipulation of differences can be used to achieve Difference Closure, thereby confirming claims of causal efficacy and also eliminating possible confounds. The model of Differential Inference outlined here shows at least one way in which it is possible to ‘reason from the phenomena’. (shrink)

The void is deadly. If you were cast into a void, it would cause you to die in just a few minutes. It would suck the air from your lungs. It would boil your blood. It would drain the warmth from your body. And it would inflate enclosures in your body until they burst}.