This paper argues that the semantic facts about ‘because’ are best explained via a metaphorical treatment of metaphysical explanation that treats causal explanation as explanation par excellence. Along the way, it defends a commitment to a unified causal sense of ‘because’ and offers a proprietary explanation of grounding skepticism. With the causal metaphor account of metaphysical explanation on the table, an extended discussion of the relationship between conceptual structure and metaphysics ends with a suggestion that the semantic facts about ‘because’ (...) tell against grounding-causation unity. (shrink)

This paper considers the applicability of standard accounts of causation to living systems. In particular it examines critically the increasing tendency to equate causal explanation with the identification of a mechanism. A range of differences between living systems and paradigm mechanisms are identified and discussed. While in principle it might be possible to accommodate an account of mechanism to these features, the attempt to do so risks reducing the idea of a mechanism to vacuity. It is proposed that the solution (...) to this problem requires the development of a philosophical account of process adequate to apply to living systems. (shrink)

When scientists seek further confirmation of their results, they often attempt to duplicate the results using diverse means. To the extent that they are successful in doing so, their results are said to be robust. This paper investigates the logic of such "robustness analysis" [RA]. The most important and challenging question an account of RA can answer is what sense of evidential diversity is involved in RAs. I argue that prevailing formal explications of such diversity are unsatisfactory. I propose a (...) unified, explanatory account of diversity in RAs. The resulting account is, I argue, truer to actual cases of RA in science; moreover, this account affords us a helpful, new foothold on the logic undergirding RAs. (shrink)

The increasing availability of large‐scale, complex data has made research into how human genomes determine physiology in health and disease, as well as its application to drug development and medicine, an attractive field for artificial intelligence (AI) approaches. Looking at recent developments, we explore how such approaches interconnect and may conflict with needs for and notions of causal knowledge in molecular genetics and genomic medicine. We provide reasons to suggest that—while capable of generating predictive knowledge at unprecedented pace and scale—if (...) and how these approaches will be integrated with prevailing causal concepts will not only determine the future of scientific understanding and self‐conceptions in these fields. But these questions will also be key to develop differentiated policies, such as for education and regulation, in order to harness societal benefits of AI for genomic research and medicine. (shrink)

Traditional debates about scientific realism tend to focus on issues concerning scientific representation and de-emphasize issues concerning scientific intervention. Questions about the relation between theories and the world, the nature of scientific inference, and the structure of scientific explanations have occupied a central place in the realism debate, while questions about experimentation and technology have not. Ian Hacking's experimental realism attempts to reverse this trend by shifting the defense of realism away from representation to intervention. Experimental realism, according to Hacking, (...) does not require us to believe that our theories are true, nor does its defense depend on inference to the best explanation. For Hacking, the strongest proof for realism is that we can manipulate objects: 'So far as I'm concerned, if you can spray them, then they are real'. (shrink)

Philosophers have traditionally treated physicalism as an empirically informed metaphysical thesis. This approach faces a well-known problem often referred to as Hempel’s dilemma: formulations of physicalism tend to be either false or indeterminate. The generally preferred strategy to address this problem involves an appeal to a hypothetical complete and ideal physical theory. After demonstrating that this strategy is not viable, I argue that we should redefine physicalism as an interdisciplinary research program seeking to explain the mental in terms of the (...) physical that encompasses the physical sciences, the psychological and brain sciences, and philosophy. Redefining physicalism in this way improves upon previous reconstructive accounts while avoiding the indeterminacy associated with orthodox forms of futurist physicalism. (shrink)

In the recent philosophy of explanation, a growing attention to and discussion of non-causal explanations has emerged, as there seem to be compelling examples of non-causal explanations in the sciences, in pure mathematics, and in metaphysics. I defend the claim that the counterfactual theory of explanation (CTE) captures the explanatory character of both non-causal scientific and metaphysical explanations. According to the CTE, scientific and metaphysical explanations are explanatory by virtue of revealing counterfactual dependencies between the explanandum and the explanans. I (...) support this claim by illustrating that CTE is applicable to Euler’s explanation (an example of a non-causal scientific explanation) and Loewer’s explanation (an example of a non-causal metaphysical explanation). (shrink)

Biologists in many different fields of research give how-possibly explanations of the phenomena they study. Although such explanations lack empirical support, and might be regarded by some as unscientific, they play an important heuristic role in biology by helping biologists develop theories and concepts and suggesting new areas of research. How-possibly explanations serve as a useful framework for conducting research in the absence of adequate empiri cal data, and they can even become how-actually explanations if they gain enough empirical support.

Can phenomenal consciousness be given a reductive natural explanation? Exponents of an.

Are causal explanations (e.g., “she switched careers because of the COVID pandemic”) treated differently from the corresponding claims that one factor caused another (e.g., “the COVID pandemic caused her to switch careers”)? We examined whether explanatory and causal claims diverge in their responsiveness to two different types of information: covariation strength and mechanism information. We report five experiments with 1,730 participants total, showing that compared to judgments of causal strength, explanatory judgments tend to bemoresensitive to mechanism andlesssensitive to covariation – (...) even though explanatory judgments respond to both types of information. We also report exploratory comparisons to judgments of understanding, and discuss implications of our findings for theories of explanation, understanding, and causal attribution. These findings shed light on the potentially unique role of explanation in cognition. (shrink)

We propose that children employ specialized cognitive systems that allow them to recover an accurate “causal map” of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously understood in terms of the formalism of directed graphical causal models, or “Bayes nets”. Children’s causal learning and inference may involve computations similar to those for learning causal Bayes nets and for predicting with them. Experimental results suggest that 2- to 4-year-old children (...) construct new causal maps and that their learning is consistent with the Bayes net formalism. (shrink)

This paper reviews the debate on the notion of biological function and on functional explanation as this takes place in philosophy. It describes the different perspectives, issues, intuitions, theories and arguments that have emerged. The author shows that the debate has been too heavily influenced by the concerns of a naturalistic philosophy of mind and argues that in order to improve our understanding of biology the attention should be shifted from the study of intuitions to the study of the actual (...) practice of biological inquiry. (shrink)

How are scientific explanations possible in ecology, given that there do not appear to be many—if any—ecological laws? To answer this question, I present and defend an account of scientific causal explanation in which ecological generalizations are explanatory if they are invariant rather than lawlike. An invariant generalization continues to hold or be valid under a special change—called an intervention—that changes the value of its variables. According to this account, causes are difference-makers that can be intervened upon to manipulate or (...) control their effects. I apply the account to ecological generalizations to show that invariance under interventions as a criterion of explanatory relevance provides interesting interpretations for the explanatory status of many ecological generalizations. Thus, I argue that there could be causal explanations in ecology by generalizations that are not, in a strict sense, laws. I also address the issue of mechanistic explanations in ecology by arguing that invariance and modularity constitute such explanations. (shrink)

The formal definition of species as explanatory hypotheses presented by Fitzhugh is emended. A species is an explanatory account of the occurrences of the same character among gonochoristic or cross-fertilizing hermaphroditic individuals by way of character origin and subsequent fixation during tokogeny. In addition to species, biological systematics also employs hypotheses that are ontogenetic, tokogenetic, intraspecific, and phylogenetic, each of which provides explanatory hypotheses for distinctly different classes of causal questions. It is suggested that species hypotheses can not be applied (...) to organisms with obligate asexual, parthenogenetic, and self-fertilizing modes of reproduction. Hypotheses explaining shared characters among such organisms are, instead, strictly phylogenetic. Several implications of this emended definition are examined, especially the relations between species, intraspecific, and phylogenetic hypotheses, as well as the limitations of species names to be applied to temporally different characters within populations. (shrink)

In my 1984 book on The Foundations of Psychoanalysis, I addressed two main questions: Are the analyst’s observations in the clinical setting reliable as ‘data,’ and if so, can they actually support the major hypotheses of the theory of repression or psychic conflict, which is the cornerstone of the psychoanalytic edifice, as we know? In the book, I argued for giving a negative answer to both of these questions. Clearly, if the evidence from the couch is unreliable from the outset, (...) then this defect alone suffices to jeopardize the very foundations of the clinical theory. But, as I strongly emphasized, even if clinical data were not contaminated by the analyst’s influence, the inability of the psychoanalytic method of clinical investigation by free association to warrant the required sort of causal inferences leaves the major pillars of the theory of psychic conflict ill-supported. Thus, I see a two-fold threat to the psychoanalytic case-study method as a means of scientific inquiry.It is an immediate corollary of my challenge that it applies not only to Freud’s own original hypotheses, but also to any and all post-Freudian versions of psychoanalysis that rely on his clinical methods of validating causal inferences, though the specific content of their theories of psychic conflict is different. After all, the alteration in the content of the hypotheses hardly makes their validation more secure. Therefore, as Morris Eagle documented in a recent publication, those analysts who have objected to my critique as anachronistic have simply not come to grips with it. For example, such inadequate engagement is present, in my view, in the recent Freud Anniversary Lecture ‘Psychoanalysis as a Science: A Response to the New Challenges,’ given by Robert Wallerstein, the current president of the International Psychoanalytical Association. As he tells us, ‘The Freud Anniversary Lecture was intended primarily as a response to Grünbaum.’ Yet he does not come to grips at all with the gravamen of my challenge: Even if clinical data could be taken at face value as being uncontaminated epistemically, the inability of the psychoanalytic method of clinical investigation by free association to warrant causal inferences leaves the major pillars of the clinical theory of repression ill-supported. (shrink)

I shall argue in this paper that extant definitions of causality are incorrect since they do not cohere in the proper way with the concept of a cause of the universe. This lack of coherence is twofold. For some extant definitions of a cause, there are possible instances of the concept of a cause of the universe that do not satisfy the definitions. For these or other extant definitions, there are entities or occurrences that are not instances of the concept (...) of a cause of the universe but that count as causes of the universe according to the definitions. This failure of coherence between extant definitions of causality and the concept of a cause of the universe entails either that there is some other definition of a cause that is correct or that causality is indefinable. I shall suggest in the conclusion of this paper that it is more plausible to believe that causality is indefinable. (shrink)

In How the Laws of Physics Lie (1983)2 Cartwright argues for being a realist about theoretical entities but non-realist about theoretical laws. Her reason for this distinction is that only the former involves causal explanation, and accepting causal explanations commits us to the existence of the causal entity invoked. “What is special about explanation by theoretical entity is that it is causal explanation, and existence is an internal characteristic of causal claims. There is nothing similar for theoretical laws.” (p. 93). (...) For, according to Cartwright, the acceptability of a theoretical explanation is a matter of its ability to satisfy such criteria as prganizing and simplifying, and in her view, “success at organizing, predicting, and classifying is never an argument for truth.” (p. 91). In contrast, Cartwright claims, “When I infer from an effect to a cause, I am asking what made the effect occur, what brought it about. (shrink)