Causal patterns and adequate explanations

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.

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.

Philosophy and Phenomenological Research

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

Foundations of Science, 2019

Explanation" refers to a wide range of activities, with a family resemblance between them. Most satisfactory explanations in a discipline for a domain fail to satisfy some general desiderata, while fulfilling others. This can happen in various ways. Why? An idealizing response would be to say that in real science explanations fall short along some dimensions, so that for any explanatory failure there is a conceivable improvement that addresses its shortcomings. The improvement may be more accurate causally or possess more unifying power, or deliver deeper understanding. We formulate a drastically less idealizing response. We argue that there are typically trade-offs in explanation, so that in strengthening one explanatory virtue one will usually weaken another. Scientific explanations, if this is correct, are constrained by such trade-offs. Particular trade-offs are appropriate for particular explanatory vehicles. There are the overarching equations of theoretical physics, which produce unification at the expense of causal detail; there are theoretical models of phenomena that occupy a middle ground between generality and the detailed workings of particular cases and get closer to explaining the workings of specific systems at the expense of unification. Sometimes experiments aim at general causal patterns at the expense of particular detail; and sometimes they are designed to give us information of particular detail at the expense of generality. There are further trade-offs associated with other vehicles of explanation. We provide examples from physics and biology.

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.

We defend a pragmatic approach in the philosophy of causation and explanation. Our approach is grounded in the more general pragmatic stance that we take towards the goals of the sciences. By means of our pragmatic view on explanation and causal reasoning we primarily want to do justice to the diversity in scientific practice. We show how this approach leads us to the defence of explanatory and causal pluralism. We further argue that a pragmatic approach, at least in the philosophy of causation and explanation, can lead to knowledge that is better achievable, more interesting, and more useful for practice, in comparison with the traditional approach which is routed in monistic presuppositions and which denies the diversity of scientific practice.

Social Science Research Network, 2002

I shall endeavor to show that every physical theory since Newton explains without drawing attention to causes-that, in other words, physical theories as physical theories aspire to explain under an ideal quite distinct from that of causal explanation. If I am right, then even if sometimes the explanations achieved by a physical theory are not in violation of the standard of causal explanation, this is purely an accident. For physical theories, as I will show, do not, as such, aim at accommodating the goals or aspirations of causal explanation. This will serve as the founding insight for a new theory of explanation, which will itself serve as the cornerstone of a new theory of scientific method.

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.

2013

This article compares causal and constitutive explanation. While scientific inquiry usually addresses both causal and constitutive questions, making the distinction is crucial for a detailed understanding of scientific questions and their interrelations. These explanations have different kinds of explananda and they track different sorts of dependencies. Constitutive explanations do not address events or behaviors, but causal capacities. While there are some interesting relations between building and causal manipulation, causation and constitution are not to be confused. Constitution is a synchronous and asymmetric relation between relata that cannot be conceived as independent existences. However, despite their metaphysical differences, the same key ideas about explanation largely apply to both. Causal and constitutive explanations face similar challenges (such as the problems of relevance and explanatory regress) and both are in the business of mapping networks of counterfactual dependence – i.e. mechanisms – although the relevant counterfactuals are of a different sort. In the final section the issue of developmental explanation is discussed. It is argued that developmental explanations deserve their own place in taxonomy of explanations, although ultimately developmental dependencies can be analyzed as combinations of causal and constitutive dependencies. Hence, causal and constitutive explanation are distinct, but not always completely separate forms of explanation.