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)

Woodward's long awaited book is an attempt to construct a comprehensive account of causation explanation that applies to a wide variety of causal and explanatory claims in different areas of science and everyday life. The book engages some of the relevant literature from other disciplines, as Woodward weaves together examples, counterexamples, criticisms, defences, objections, and replies into a convincing defence of the core of his theory, which is that we can analyse causation by appeal to the notion of manipulation.

This book highlights some of the conceptual problems that still need to be solved and points out a number of fresh philosophical ideas to explore.

Woodward's long awaited book is an attempt to construct a comprehensive account of causation explanation that applies to a wide variety of causal and explanatory claims in different areas of science and everyday life. The book engages some of the relevant literature from other disciplines, as Woodward weaves together examples, counterexamples, criticisms, defenses, objections, and replies into a convincing defense of the core of his theory, which is that we can analyze causation by appeal to the notion of manipulation.

Causality offers the first comprehensive coverage of causal analysis in many sciences, including recent advances using graphical methods. Pearl presents a unified account of the probabilistic, manipulative, counterfactual and structural approaches to causation, and devises simple mathematical tools for analyzing the relationships between causal connections, statistical associations, actions and observations. The book will open the way for including causal analysis in the standard curriculum of statistics, artificial intelligence, business, epidemiology, social science and economics.

Wesley Salmon is renowned for his seminal contributions to the philosophy of science. He has powerfully and permanently shaped discussion of such issues as lawlike and probabilistic explanation and the interrelation of explanatory notions to causal notions. This unique volume brings together twenty-six of his essays on subjects related to causality and explanation, written over the period 1971-1995. Six of the essays have never been published before and many others have only appeared in obscure venues. The volume includes a section (...) of accessible introductory pieces, as well as more advanced and technical pieces, and will make essential work in the philosophy of science readily available to both scholars and students. (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.

In this chapter I will employ a well-known scientific research heuristic that studies how something works by focusing on circumstances in which it does not work. Rather than trying to describe what scientific understanding would ideally look like, I will try to learn something about it by observing mundane cases where understanding is partly illusory. My main thesis is that scientists are prone to the illusion of depth of understanding (IDU), and as a consequence they sometimes overestimate the detail, coherence, (...) and depth of their understanding. I will analyze the notion of understanding and its relation to a sense of understanding. In order to make plausible the claim that these are often disconnected, I will describe an interesting series of psychological experiments by Frank Keil and his coauthors that suggests that ordinary people routinely overestimate the depth of their understanding. en I will argue that we should take seriously the possibility that scientific cognition is also aðected by IDU and spell out some possible causes of explanatory illusions in science. I will conclude this chapter by discussing how scientific explanatory practices could be improved and how the philosophy of science might be able to contribute to this process. (shrink)

This paper is an attempt to further our understanding of mechanisms conceived of as ontologically separable from laws. What opportunities are there for a mechanistic perspective to be independent of, or even more fundamental than, a law perspective? Advocates of the mechanistic view often play with the possibility of internal and external reliability, or with the paralleling possibilities of enforcing, counteracting, redirecting, etc., the mechanisms’ power to produce To further this discussion I adopt a trope ontology. It is independent of (...) the notion of law, and can easily be adapted to account for such characteristics of mechanisms. The idea of tropes as mechanisms is worked out in some detail. According to the resulting picture, there is still an opportunity to link mechanisms and laws. But while the predominant law view conceives of mechanistic approaches as special kinds of law accounts, this study indicates that the converse may be true. Law accounts are special cases of mechanistic accounts, and they work only in those worlds where the mechanisms are of the right kind. (shrink)

Studdert-Kennedy, Gerald, Evidence and Explanation in Social Science. ... Kauffman, Stuart, "Articulation of Parts Explanation in Biology and the Rational ...

In this bold work of broad scope and rich erudition, Richard W. Miller sets out to reorient the philosophy of science.

I discussed the ways in which folk psychology is influenced by the need for small-scale cooperation between people. I argue that considerations about cooperation and mutual benefit can be found in the everyday concepts of belief, desire, and motivation. I describe what I call "solution thinking", where a person anticipates another person's actions by first determining the solution to the cooperative problem that the person faces and then reasoning backwards to a prediction of individual action.

(Journal of Philosophy, 1988, 85:303-318).