How do we go about weighing evidence, testing hypotheses, and making inferences? According to the model of _Inference to the Best Explanation_, we work out what to infer from the evidence by thinking about what would actually explain that evidence, and we take the ability of a hypothesis to explain the evidence as a sign that the hypothesis is correct. In _Inference to the Best Explanation_, Peter Lipton gives this important and influential idea the development and assessment it deserves. The (...) second edition has been substantially enlarged and reworked, with a new chapter on the relationship between explanation and Bayesianism, and an extension and defence of the account of contrastive explanation. It also includes an expanded defence of the claims that our inferences really are guided by diverse explanatory considerations, and that this pattern of inference can take us towards the truth. This edition of _Inference to the Best Explanation_ has also been updated throughout and includes a new bibliography. (shrink)

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)

Ethics and jurisprudence are liable to the remark in common with logic. Almost every writer having taken a different view of some of the particulars which ...

Commonly we distinguish the strike of a match, as a cause of the match lighting, from the presence of oxygen, as a mere condition. In this paper I propose an account of this phenomenon, which I call causal selection. I suggest some reasons for taking causal selection seriously, and indicate some shortcomings of the popular contrastive approach. Chief among these is the lack of an account of contrast choice. I propose that contrast choice is often just the counterfactual scenario in (...) which the effect does not occur: I suggest that if c causes e , then if e hadn't occurred, c wouldn't have occurred. I argue that this is a necessary condition on causation which causes meet but mere conditions fail. (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.

The present article identifies how social determinants of health raise two categories of philosophical problems that also fall within the smaller domain of ethics; one set pertains to the philosophy of epidemiology, and the second set pertains to the philosophy of health and social justice. After reviewing these two categories of ethical concerns, the limited conclusion made is that identifying and responding to social determinants of health requires inter-disciplinary reasoning across epidemiology and philosophy. For the reasoning used in epidemiology to (...) be sound, for its scope and (moral) purpose as a science to be clarified as well as for social justice theory to be relevant and coherent, epidemiology and philosophy need to forge a meaningful exchange of ideas that happens in both directions. (shrink)

In What is Disease?, renowned philosophers and medical ethicists survey and elucidate the profoundly important concepts of disease and health. Christopher Boorse begins with an extensive reexamination of his seminal definition of disease as a value-free scientific concept. In responding to all those who criticized this view, which came to be called "naturalism" or "neutralism," Boorse clarifies and updates his landmark ideas on this crucial question. Other distinguished thinkers analyze, develop, and oftentimes defend competing, nonnaturalistic theories of disease, including discussions (...) of the relevance of these concepts to the question of "diseased" sexual orientation and to alternative medicine. What is Disease? brings concerned readers up-to-date in the debate over the proper definition of "disease," a concept of central importance not only for bioethicists, but also for those throughout clinical medicine, sociology, psychology, and law who deal with disease and its associated problems on an everyday basis. (shrink)

An updated and extended second edition supporting the findings of its well-known predecessor which claimed that courts employ common-sense notions of causation in determining legal responsibility.