Erik Weber

Remote Causes, Bad Explanations?

Amidst both philosophers of science and social scientists (including historians) one finds people defending the thesis that explanations of particular facts should refer to proximate causes, or that explanations referring to remote causes are at least less-if they are any-good (e.g. Jon Elster, Paul Veyne, Michael Taylor, etc.). The idea is that any explanatory factor at a temporal remove from the fact to be explained, should be replaced by a factor closer to the fact. This close-grain preference is a matter of favoring explanations that provide the detailed mediating mechanisms in non-interrupted causal chains across time. The claims we want to defend in this paper are: (1) explanations of plain facts (as opposed to explanations of contrasts between facts) that invoke remote causes on top of proximate causes are often better than explanations that invoke only proximate causes; and. (2) explanations of contrasts between facts that invoke only proximate cause are often worthless: one has to invoke a remote cause in order to provide a minimally adequate contrastive explanation. The upshot of this is that the close-grain preference should be replaced with a more differentiated explanatory pluralism.

Explanatory pluralism in the medical sciences: Theory and practice

Explanatory pluralism is the view that the best form and level of explanation depends on the kind of question one seeks to answer by the explanation, and that in order to answer all questions in the best way possible, we need more than one form and level of explanation. In the first part of this article, we argue that explanatory pluralism holds for the medical sciences, at least in theory. However, in the second part of the article we show that medical research and practice is actually not fully and truly explanatory pluralist yet. Although the literature demonstrates a slowly growing interest in non-reductive explanations in medicine, the dominant approach in medicine is still methodologically reductionist. This implies that non-reductive explanations often do not get the attention they deserve. We argue that the field of medicine could benefit greatly by reconsidering its reductive tendencies and becoming fully and truly explanatory pluralist. Nonetheless, trying to achieve the right balance in the search for and application of reductive and non-reductive explanations will in any case be a difficult exercise.

How Probabilistic Causation Can Account for the Use of Mechanistic Evidence

In a recent article in this journal, Federica Russo and Jon Williamson argue that an analysis of causality in terms of probabilistic relationships does not do justice to the use of mechanistic evidence to support causal claims. I will present Ronald Giere’s theory of probabilistic causation, and show that it can account for the use of mechanistic evidence (both in the health sciences—on which Russo and Williamson focus—and elsewhere). I also review some other probabilistic theories of causation (of Suppes, Eells, and Humphreys) and show that they cannot account for the use of mechanistic evidence. I argue that these theories are also inferior to Giere’s theory in other respects.

Explanation And Thought Experiments In History

Although interest in them is clearly growing, most professional historians do not accept thought experiments as appropriate tools. Advocates of the deliberate use of thought experiments in history argue that without counterfactuals, causal attributions in history do not make sense. Whereas such arguments play upon the meaning of causation in history, this article focuses on the reasoning processes by which historians arrive at causal explanations. First, we discuss the roles thought experiments play in arriving at explanations of both facts and contrasts. Then, we pinpoint the functions thought experiments fulfill in arriving at weighted explanations of contrasts.

Social Mechanisms, Causal Inference, and the Policy Relevance of Social Science:

The paper has two aims. First, to show that we need social mechanisms to establish the policy relevance of causal claims, even if it is possible to build a good argument for those claims without knowledge of mechanisms. Second, to show that although social scientists can, in principle, do without social mechanisms when they argue for causal claims, in reality scientific practice contexts where they do not need mechanisms are very rare.

The Covering Law Model Applied to Dynamical Cognitive Science: A Comment on Joel Walmsley

In a 2008 paper, Walmsley argued that the explanations employed in the dynamical approach to cognitive science, as exemplified by the Haken, Kelso and Bunz model of rhythmic finger movement, and the model of infant preservative reaching developed by Esther Thelen and her colleagues, conform to Carl Hempel and Paul Oppenheim’s deductive-nomological model of explanation (also known as the covering law model). Although we think Walmsley’s approach is methodologically sound in that it starts with an analysis of scientific practice rather than a general philosophical framework, we nevertheless feel that there are two problems with his paper. First, he focuses only on the deductivenomological model and so neglects the important fact that explanations are causal. Second, the explanations offered by the dynamical approach do not take the deductive-nomological format, because they do not deduce the explananda from exceptionless laws. Because of these two points, Walmsley makes the dynamical explanations in cognitive science appear problematic, while in fact they are not.

Unification And Explanation

In this article we criticize two recent articles that examinethe relation between explanation and unification. Halonen and Hintikka (1999), on the one hand,claim that no unification is explanation. Schurz (1999), on the other hand, claims that all explanationis unification. We give counterexamples to both claims. We propose a pluralistic approach to the problem:explanation sometimes consists in unification, but in other cases different kinds of explanation(e.g., causal explanation) are required; and none of these kinds is more fundamental.

Explaining, understanding and scientific theories

One of the functions of scientific knowledge is to provide the theories and laws we need in order to understand the world. My article deals with the epistemic aspect of understanding, i.e., with understanding as unification. The aim is to explicate what we have to do in order to make our scientific knowledge contribute to an increase of the degree to which the particular events we have observed, fit into our world-picture. The analysis contains two parts. First I define the concept of scientific epistemic explanation. Explanations of these type are the appropriate instruments for increasing the degree of unification of the particular events we have observed. In the second, largest part of the article I analyze the construction process of scientific epistemic explanations, focusing on the application of scientific theories.

Plausibility versus richness in mechanistic models

In this paper we argue that in recent literature on mechanistic explanations, authors tend to conflate two distinct features that mechanistic models can have or fail to have: plausibility and richness. By plausibility, we mean the probability that a model is correct in the assertions it makes regarding the parts and operations of the mechanism, i.e., that the model is correct as a description of the actual mechanism. By richness, we mean the amount of detail the model gives about the actual mechanism. First, we argue that there is at least a conceptual reason to keep these two features distinct, since they can vary independently from each other: models can be highly plausible while providing almost no details, while they can also be highly detailed but plainly wrong. Next, focusing on Cravers continuum of “how-possibly,” to “how-plausibly,” to “how-actually” models, we argue that the conflation of plausibility and richness is harmful to the discussion because it leads to the view that both are necessary for a model to have explanatory power, while in fact, richness is only so with respect to a mechanisms activities, not its entities. This point is illustrated with two examples of functional models.

Unification: What is it, how do we Reach and why do we Want it?

This article has three aims. The first is to give a partial explication of the concept of unification. My explication will be partial because I confine myself to unification of particular events, because I do not consider events of a quantitative nature, and discuss only deductive cases. The second aim is to analyze how unification can be reached. My third aim is to show that unification is an intellectual benefit. Instead of being an intellectual benefit unification could be an intellectual harm, i.e., a state of mind we should try to avoid by all means. By calling unification an intellectual benefit, we claim that this form of understanding has an intrinsic value for us. I argue that unification really has this alleged intrinsic value.