The nature of scientific explanation is controversial. Some maintain that all scientific explanations have to be contrastive in nature (contrastivism). However, others argue that no scientific explanation is genuinely contrastive (non-contrastivism). In addition, a compatibilist view has been recently devloped. It is argued that the debate between contrastivism and non-contrastivism is merely a linguistic dispute rather than a genuine disagreement on the nature of scientific explanation. Scientific explanations are both contrastive and non-contrastive in some sense (compatibilism). This paper examines the (...) debate between contrastivism and non-contrastivism in scientific explanation. It begins with a critical review of the arguments for contrastivism, for non-contrastivism, and for compatibilism and concludes with some remarks on the prospect of the issue. (shrink)

This paper provides an inferentialist account of model-based understanding by combining a counterfactual account of explanation and an inferentialist account of representation with a view of modeling as extended cognition. This account makes it understandable how the manipulation of surrogate systems like models can provide genuinely new empirical understanding about the world. Similarly, the account provides an answer to the question how models, that always incorporate assumptions that are literally untrue of the model target, can still provide factive explanations. Finally, (...) the paper shows how the contrastive counterfactual theory of explanation can provide tools for assessing the explanatory power of models. (shrink)

Comparisons of rival explanations or theories often involve vague appeals to explanatory power. In this paper, we dissect this metaphor by distinguishing between different dimensions of the goodness of an explanation: non-sensitivity, cognitive salience, precision, factual accuracy and degree of integration. These dimensions are partially independent and often come into conflict. Our main contribution is to go beyond simple stipulation or description by explicating why these factors are taken to be explanatory virtues. We accomplish this by using the contrastive-counterfactual approach (...) to explanation and the view of understanding as an inferential ability. By combining these perspectives, we show how the explanatory power of an explanation in a given dimension can be assessed by showing the range of answers it provides to what-if-things-had-been-different questions and the theoretical and pragmatic importance of these questions. Our account also explains intuitions linking explanation to unification or to exhibition of a mechanism. (shrink)

Ian Hacking uses the looping effect to describe how classificatory practices in the human sciences interact with the classified people. While arguably this interaction renders the affected human kinds unstable and hence different from natural kinds, realists argue that also some prototypical natural kinds are interactive and human kinds in general are stable enough to support explanations and predictions. I defend a more fine-grained realist interpretation of interactive human kinds by arguing for an explanatory domain account of the looping effect. (...) First, I argue that knowledge of the feedback mechanisms that mediate the looping effect can supplement, and help to identify, the applicability domain over which a kind and its property variations are stably explainable. Second, by applying this account to cross-cultural case studies of psychiatric disorders, I distinguish between congruent feedback mechanisms that explain matches between classifications and kinds, and incongruent feedback mechanisms that explain mismatches. For example, congruent mechanisms maintain Western auditory experiences in schizophrenia, whereas exporting diagnostic labels inflicts incongruence by influencing local experiences. Knowledge of the mechanisms can strengthen explanatory domains, and thereby facilitate classificatory adjustments and possible interventions on psychiatric disorders. (shrink)

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. (shrink)

Not only has the philosophical debate on causation been gaining ground in the last few decades, but it has also increasingly addressed the sciences. The biomedical sciences are among the most prominent fields that have been considered, with a number of works tackling the understanding of the notion of cause, the assessment of genuinely causal relations and the use of causal knowledge in applied contexts. Far from denying the merits of the debate on causation and the major theories it comprises, (...) this paper is meant as a stimulus for theorists of causation in the philosophy of biomedicine, with a focus on clinical matters. Without aiming at putting forward an original theory of causation and starting from the narration of two actual but paradigmatic cases at the joints between biomedical research and clinical practice, we want to point out that some pathological situations addressed by molecular medicine actually prove resistant to some of our major epistemological accounts of causal explanation. Given this scenario, which is very frequent in our hospitals, our analysis aims to provide a stimulus for the debate among theorists of causation in biomedicine interested in real cases in science in practice. We believe that this might in turn encourage some more general rethinking of the complex intertwinement of science, philosophy of science and ethics, as well as of the role of philosophy of science for clinical medicine itself. (shrink)

Two apparently mutually exclusive ideas about the relation between contrastive and non-contrastive explanations can be found in the literature. According to contrastivists, all explanation is contrastive explanation and the supposed existence of non-contrastive explanations can be revealed to be an illusion. According to non-contrastivists, on the other hand, contrastive explanation can be fully analysed in terms of non-contrastive explanation, and is thus not of fundamental importance. In the current article, I discuss the main arguments in favour of and against each (...) of the two positions. This discussion leads to the idea that contrastive explanations are to be understood as parts of a bigger, more complete non-contrastive explanation; but that all actual explanations explain only a limited set of contrasts. I conclude that, once the relation between contrastive and non-contrastive explanations is understood correctly, there remains no substantial issue to divide contrastivism and non-contrastivism. (shrink)

To give a causal explanation is to give information about causal history. But a vast amount of causal history lies behind anything that happens, far too much to be included in any intelligible explanation. This is the Problem of Limitation for explanatory information. To cope with this problem, explanations must select for what is relevant to and adequate for answering particular inquiries. In the present paper this idea is used in order to distinguish two kinds of causal explanation, on the (...) grounds of systematic differences in their conditions of relevance and adequacy. It is further argued that these two forms of causal explanation are interdependent and their interaction provides an instrument through which causal knowledge is acquired and enhanced. What we understand causation in the world to be is neither unconditioned regularity, nor counterfactual dependence, but the sum of correct answers to explanatory inquiries of these two interdependent kinds. (shrink)