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We present a logic, $$\mathbf {ELI^r}$$ , for the discovery of deterministic causal regularities starting from empirical data. Our approach is inspired by Mackie's theory of causes as INUS-conditions, and implements a more recent adjustment to Mackie's theory according to which the left-hand side of causal regularities is required to be a minimal disjunction of minimal conjunctions. To derive such regularities from a given set of data, we make use of the adaptive logics framework. Our knowledge of deterministic causal regularities is, as Mackie noted, most often gappy or elliptical. The adaptive logics framework is well-suited to explicate both the internal and the external dynamics of the discovery of such gappy regularities. After presenting $$\mathbf {ELI^r}$$ , we first discuss these forms of dynamics in more detail. Next, we consider some criticisms of the INUS-account and show how our approach avoids them, and we compare $$\mathbf {ELI^r}$$ with the CNA algorithm that was recently proposed by Michael Baumgartner. [ABSTRACT FROM AUTHOR]

What are scientific theories and how should they be represented? In this article, I propose a causal–structural account, according to which scientific theories are to be represented as sets of interrelated causal and credal nets. In contrast with other accounts of scientific theories (such as Sneedian structuralism, Kitcher’s unificationist view, and Darden’s theory of theoretical components), this leaves room for causality to play a substantial role. As a result, an interesting account of explanation is provided, which sheds light on explanatory unification within a causalist framework. The theory of classical genetics is used as a case study.1 Introduction2 The Theory of Classical Genetics3 Three Philosophical Accounts of the Theory of Classical Genetics 3.1 The structuralist account 3.2 Kitcher’s unificationism 3.3 Darden and theory change in science4 A Common Lacuna: Where is Causality?5 Woodward’s Interventionist Account of Causation6 Causal Bayes Nets and Their Interrelations 6.1 Causal Bayes nets 6.2 Relations among causal nets 6.3 Credal nets and their interrelations7 The Theory of the Gene and its Causal Graph8 A First Exemplar: Stem Length in Pea Plants 8.1 Three crosses on stem length in pea plants 8.2 The causal graph for stem length in pea plants 8.3 Morgan’s explanatory principles and the credal net for stem length in pea plants9 Explaining Mendel’s Crosses: A Causal–Structural Account10 Monohybrid Crosses with Complete Dominance11 Exemplars, Abstract Explanatory Patterns, Generic Credal Nets, and Mechanism Schemas12 Incomplete Dominance13 Anomalies14 Multi-hybrid Crosses with Independent Assortment15 Multi-hybrid Crosses with Linkage and Crossing-Over16 Double Crossing-Over and the Linear Order of the Gene17 Causal–Structural Explanation18 Explanatory Unification19 Concluding Remarks [ABSTRACT FROM PUBLISHER]

Mechanistic philosophy of science views a large part of scientific activity as engaged in modelling mechanisms. While science textbooks tend to offer qualitative models of mechanisms, there is increasing demand for models from which one can draw quantitative predictions and explanations. Casini et al. (Theoria 26(1):5-33, ) put forward the Recursive Bayesian Networks (RBN) formalism as well suited to this end. The RBN formalism is an extension of the standard Bayesian net formalism, an extension that allows for modelling the hierarchical nature of mechanisms. Like the standard Bayesian net formalism, it models causal relationships using directed acyclic graphs. Given this appeal to acyclicity, causal cycles pose a prima facie problem for the RBN approach. This paper argues that the problem is a significant one given the ubiquity of causal cycles in mechanisms, but that the problem can be solved by combining two sorts of solution strategy in a judicious way. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports within the issue on topics including causation in biology systems, causality in physics, and philosophical mechanism in the context of ontic and epistemic notions.

The use of general and universal laws in historiography has been the subject of debate ever since the end of the nineteenth century. Since the 1970s there has been a growing consensus that general laws such as those in the natural sciences are not applicable in the scientific writing of history. We will argue against this consensus view, not by claiming that the underlying conception of what historiography is-or should be-is wrong, but by contending that it is based on a misconception of what general laws such as those of the natural sciences are. We will show that a revised notion of law, one inspired by the work of Sandra D. Mitchell, in tandem with Jim Woodward's notion of 'invariance,' is indeed applicable to historiography, much in the same way as it is to most other scientific disciplines. Having developed a more adequate account of general laws, we then show, by means of three examples, that what are called 'pragmatic laws' and 'invariance' do in fact play a role in history in several interesting ways. These examples-from cultural history, economic history, and the history of religion-have been selected on the basis of their diversity in order to illustrate the widespread use of pragmatic laws in history. [ABSTRACT FROM AUTHOR]

Editors' introduction to the special issue on the Causality and Explanation in the Sciences conference, held at the University of Ghent in September 2011. [ABSTRACT FROM AUTHOR]

An essay is presented on the practical value of causal knowledge and its potential to produce a change in effect. The authors discuss the distinction between "manipulative policy" and "selective policy," and they present a theoretical framework to argue for wider application and use of practical value. Philosophers including Nancy Cartwright and Daniel Hausman are mentioned, as are the concepts of "effective strategy," laws of association, and causal laws.