Causality and Explanation in the Sciences: the Rest of the Best

THEORIA. An International Journal for Theory, History and Foundations of Science, 2012

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

Humana.Mente: Journal of Philosophical Studies, 2015

After a concise description of issues concerning the causal and the deductive-nomological models of explanation, the flaws in the alternative view centred on relevance-to-context are examined. The paper argues for the need of a wider spectrum of options which takes into account both the Local/Global and the Internal/External aspects in order to determine the sense and the adequacy of any explanation. As a test for this argument, some specific problems are considered about the range of causal bonds, the admission of top-down causation, the appeal to emergence, the shift from explanation to explainability, the equivalence classes referred to as "cause" and "effect". Finally, the paper deals with the comparison between inequivalent explanations and lists three remaining issues to complete the picture.

In some scientific explanations, mathematical derivations or proofs appear to be the primary bearers of enlightenment. Is this a case, in science, of " explanation beyond causation " ? Might these explanations be causal only in part, or only in an auxiliary way, or not at all? To answer this question, I will examine some well-known examples of explanations that seem to operate largely or wholly through mathematical derivation or proof. I conclude that the mathematical and the causal components of the explanations are complementary rather than rivalrous: the function of the mathematics is to help the explanations' consumers better grasp relevant aspects of the causal structure that does the explaining, and above all, to better grasp how the structure causally makes a difference to the phenomena to be explained. The explanations are revealed, then, to be causal through and through. It does not follow that all scientific explanation is causal, but it does follow that one large and interesting collection of scientific explanations that has looked non-causal to many philosophers in fact fits closely with the right kind of causal account of explanation. In that observation lies my contribution to the present volume's dialectic.

Citeseer

This chapter is the introduction to the volume. The volume editors begin by setting out a manifesto that puts forward two theses: first, that the sciences are the best place to turn in order to understand causality; second, that scientifically-informed philosophical investigation can bring something to the sciences too. Next, the chapter goes through the various parts of the volume, drawing out relevant background to and themes of the chapters in those parts. Finally, the chapter discusses the progeny of the papers and identify some next steps for research into causality in the sciences.

2012

-- Citation information: Schurz, G., & Gebharter, A. (2012). Explanation, causality, and unification, 11–12 November [Conference report]. The Reasoner, 6(1), 9–10.

Logos Universality Mentality Education Novelty: Philosophy and Humanistic Sciences, 2017

One of our main activities, as human beings, consists of the attempt to explain and to understand what is not known (yet) by what is already known and familiar. Our explanations are often causal which is why it is frequently considered that to explain a phenomenon means to describe its causes. But we must keep in mind the idea that explaining what is new and we do not know yet through known notions is a complex and risky process. Some of the most common risks consist of the fact that sometimes, through such explanation we don't succeed to bring any extra knowledge and other times we fail to grasp the real causal connections between the phenomena, which lacks our judgments of truth value. The modifications of the concept of causality due to the new discoveries of physics added to our tendency to invent causal explanations is confusing in science as well as in philosophy. In the case of the judicial philosophy for instance, the manner in which the relations and social phenomena are understood and explained have direct influence over the legal regulation, making the law enforcement more or less efficient. In this paper we intend to analyze to what extent our willingness to provide explanations for everything that happens affects the concept of causation and whether these difficulties can be related to causal inference. In classical logic, the specialists analyzed the causal inferences and the logical rules implied in order to achieve reliable conclusions and we will refer to them with the purpose of avoiding errors.

Causality in the Sciences, 2011

Causalists about explanation claim that to explain an event is to provide information about the causal history of that event. Some causalists also endorse a proportionality claim, namely that one explanation is better than another insofar as it provides a greater amount of causal information. I consider various challenges to these causalist claims. There is a common and influential formulation of the causalist requirement—the “Causal Process Requirement”—that does appear vulnerable to these anti-causalist challenges, but I argue that they do not give us reason to reject causalism entirely. Instead, these challenges lead us to articulate the causalist requirement in an alternative way. This alternative articulation incorporates some of the important anti-causalist insights without abandoning the explanatory necessity of causal information. For example, proponents of the “equilibrium challenge” argue that the best available explanations of the behavior of certain dynamical systems do not appear to provide any causal information. I respond that, contrary to appearances, these equilibrium explanations are fundamentally causal, and I provide a formulation of the causalist thesis that is immune to the equilibrium challenge. I then show how this formulation is also immune to the “epistemic challenge”—thus vindicating (a properly formulated version of) the causalist thesis.

International Studies in the Philosophy of Science, 2015

Many philosophers now regard causal approaches to explanation as highly promising, even in physics. This is due in large part to James Woodward's influential argument that a wide range of explanations (including explanations in physics) are causal, based on his interventionist approach to causation. This article focuses on explanations, widespread in physics, involving highly idealized models. These explanations are not causal, yet they do not fall under any of the types of non-causal explanation Woodward describes. I argue that causal explanation is simply not as widespread or important in physics as Woodward and others maintain.

Erkenntnis, 2013

The mechanistic and causal accounts of explanation are often conflated to yield a 'causal-mechanical' account. This paper prizes them apart and asks: if the mechanistic account is correct, how can causal explanations be explanatory? The answer to this question varies according to how causality itself is understood. It is argued that difference-making, mechanistic, dualist and inferentialist accounts of causality all struggle to yield explanatory causal explanations, but that an epistemic account of causality is more promising in this regard. §1 The mechanistic account of explanation The mechanistic account of explanation is the cornerstone of the recent interest in mechanisms in the philosophy of science. Thus Machamer et al. (2000) begin their paper with: In many fields of science what is taken to be a satisfactory explanation requires providing a description of a mechanism. So it is not surprising that much of the practice of science can be understood in terms of the discovery and description of mechanisms (Machamer et al., 2000, pp. 1-2). Mechanistic accounts of explanation have also been put forward by Salmon (1984, 1998); Glennan (2002); Bechtel and Abrahamsen (2005); Craver (2007) and others. Note that different authors have different things in mind when they talk about mechanisms. One school of thought has it that mechanisms need to be understood as physical processes, i.e., spatiotemporally contiguous processes in which a mark or a conserved quantity is propagated between interactions (Reichenbach, 1956; Salmon, 1984, 1998; Dowe, 2000). An example of this sort of mechanism is a signal from a remote control to open a garage door: pressing the button constitutes an interaction which leads to the transmission of a signal that is propagated in such a way that it can interact with a receiver at the garage. An alternative to the physical-process view is the idea of complex-systems mechanisms (CSMs). These consist of entities and activities organised in such a way that they are responsible for some phenomenon (see, e.g., Machamer et al., 2000; Illari and Williamson, 2012). An example is the remote control mechanism itself, responsible for sending the signal that opens the garage door: this is a more-or-less stable arrangement of parts that can engage in characteristic activities that lead to the transmission of the signal. These views need not be construed as alternatives. One can also take a broad view of mechanisms, according to which mechanisms involve physical processes or complex-systems mechanisms or some combination of the two. An explanation of the garage door opening might then describe or point to: (i) the CSM for producing the signal; (ii) the physical signal itself; and (iii) the CSM for receiving the signal and opening the door. Note that two types of explanation are possible: single-case, i.e., a particular garage door opening is explained by (i-iii) together with the particular fact that the remote control was triggered in the appropriate way; or generic, i.e., garage door openings in general are explained by (i-iii). Most of the following discussion will apply to both single-case and generic explanation. A second distinction is also useful. An explanation in practice is a communication that aims to increase the understanding of an interlocutor by describing how an explanandum (a single-case event or a generic phenomenon) is produced by underlying mechanisms that the interlocutor understands or accepts better than the explanandum itself. On the other hand, an ideal explanatory text is an imaginary text that would recursively describe all the underlying mechanisms: i.e., that includes descriptions of the mechanisms that are responsible for the explanandum, other mechanisms that are responsible for the appropriate functioning of those mechanisms, and so on. The concept of an ideal explanatory text faces the bottoming-out problem: some account needs to be given as to whether there is a lowest level of

Artificial neural networks (ANNs) were originally developed as mathematical models of the information processing capabilities of biological brains (McCulloch and Pitts, 1988; Rosenblatt, 1963; Rumelhart et al., 1986). Although it is now clear that ANNs bear little resemblance to real biological neurons, they enjoy continuing popularity as pattern classifiers. The basic structure of an ANN is a network of small processing units, or nodes, joined to each other by weighted connections. In terms of the original biological model, the nodes represent neurons, and the connection weights represent the strength of the synapses between the neurons. The network is activated by providing an input to some or all of the nodes, and this activation then spreads throughout the network along the weighted connections. The electrical activity of biological neurons typically follows a series of sharp 'spikes', and the activation of an ANN node was originally intended to model the average firing rate of these spikes.