With the advent of modern AI techniques, especially the brain-inspired tools known as deep neural networks, scientists can now squeeze a huge amount of utility from datasets far too big to be analyzed by hand. In disciplines as diverse as neuroscience, biochemistry, and climate science, the marriage of big data and AI has yielded extraordinary practical advancements. But the deep neural networks that are enabling these astonishing practical advances offer few clues about the nature of the data they were trained on or the intellectual basis of their predictions: They perform end runs around human insight while making far more accurate and useful predictions than our scientific theories enable. The seeds that could one day enable science to transition into a wholly practical regime, in which human understanding is construed as an unnecessary indulgence, have now been sown.

Throughout their history, philosophy and astronomy have been closely linked. Astronomy and cosmology inhabit novel epistemic spaces, and have novel epistemic aims, relative to more standard sciences. As the philosophy of astronomy emerges as an independent subdiscipline, the special nature of explanation and prediction within astronomy requires further examination and articulation. Unlike conventional experimental sciences, astronomy typically deals with entities and systems that are outside the reach of human intervention. Furthermore, astronomy and cosmology are historical sciences. This chapter evaluates the epistemology of astronomy by focusing on the canons of explanation, prediction, and theoretical success that are particular to astronomy. Demonstrations of the successes and shortcomings of philosophical models of explanation and prediction are given with respect to the eighteenth-century discovery of stellar aberration and the more recent conjectures of dark matter and dark energy.

This chapter considers the concept of proximal and distal causes in various health science fields and common reference to the “proximal–distal model” of disease causation. In many cases, this model is used to capture the interaction of social and biological factors, with social factors as distal causes and biological factors as proximal causes. The authors examine Krieger’s (2008) criticism of this framework and suggestion that proximal–distal language confuses causal thinking, reasoning, and distinctions in health science domains. While Krieger (2008) has recommended the elimination of language of proximal and distal causes, Ross and Kendler argue for another alternative, specifically that while the proximal–distal model captures some etiological scenarios, it should not be applied to all cases. They examine three ways in which social and biological causes lead to health outcomes and argue for the value (and possibility) of clear notions of proximal and distal causes.

Psychiatry has a core goal of understanding the mechanisms that produce, underlie, and maintain psychiatric disorders. But what is a mechanism? And how should the answer to that question be justified? In this chapter Craver defends a practice-first direction of fit, which involves building a notion of “mechanism” for psychiatry by charitably reconstructing what the term appears to mean from the functions it serves for psychiatrists and researchers. Taking this approach, many philosophical objections to mechanism (i.e., that it is at odds with a process metaphysics and that it is committed to reductionist obsession with detail) turn out to be predicated on misunderstandings. Some directions for a future mechanistic philosophy of psychiatry are sketched out.

This chapter introduces qualitative causal inference and explanation, a hybrid method that integrates qualitative case-study analysis with elements of quantitative causal inference. Traditional process tracing emphasizes explanation but risks false positives, while identification-based inference secures validity but offers little guidance for explaining particular outcomes. The chapter argues that adapting core ideas from causal inference – such as identification and bias – into a qualitative idiom can strengthen within-case analysis. It defines qualitative causal inference as a strategy for distinguishing true causal influences from spurious associations and sets out criteria for valid unit-level inference. By combining explanation with valid inference, this approach aims to produce more credible and more satisfying accounts of complex outcomes such as democratic transitions, revolutions, and wars.

A theory of some finding or observation is an explanation of that finding or observation. Further, a good theory is a set of principles that are sufficient to show that the phenomenon is an instance of more general phenomena or principles. But not all explanations help us understand general phenomena because they lack some fundamental characteristics. The necessary characteristics of adequate explanations include explicit definitions and precise and limited scope, that is, they do not attempt to explain everything about a given event or action. Further, they can be tested with empirical data; they do not appeal to supernatural forces or to explanations with claims that testing is not necessary.

It is sometimes claimed that there could not, in principle, be an explanation for why there is something rather than nothing, as any factor introduced in the explanation would itself be one of the things needing to be explained. In this chapter, I will argue that this idea is mistaken, and I will proceed to offer a speculative suggestion regarding the form that explanations of the relevant sort might take. The idea is that the fact that there is something rather than nothing may be explained by the obtaining of certain sorts of subjunctive conditionals, i.e., conditionals of the form “If A were the case then B would be the case.” Such subjunctive conditionals need not be grounded in any existing things, which is what makes them suitable candidates to serve in explanations for why there is something rather than nothing.

This chapter will examine whether we should expect an explanation for the Question. Does the fact that there is something rather than nothing call out for explanation? The answer to this question depends on what it takes for some phenomenon to call out for explanation. Here I will engage with some extant accounts of this notion of a phenomenon calling out for explanation, as well as the principle of sufficient reason, which in its strongest form claims that every fact must have an explanation. I will also develop my own account of when some fact calls out for explanation. I will propose that in asking whether some fact calls out for explanation we simply ask: What is the probability of that fact’s having an explanation? And here we employ the criteria of theory choice (i.e., the criteria governing our probability assignments) that we make in other contexts. To the extent that the probability that some fact has an explanation is greater than 0.5, we should expect the fact in question to have an explanation. In the case of the fact that there is something rather than nothing, the main factors relevant to determining the probability in question will be examined in subsequent chapters.

The chapter argues for a reading of Parts of Animals I.1, 639b11–640a9 as a continuous argument, divided into 3 main sections. Aristotle’s point in the first section is that teleological explanations should precede non-teleological explanations in the order of exposition. His reasoning is that the ends cited in teleological explanations are definitions, and definitions – which are not subject to further explanation – are appropriate starting points, insofar as they prevent explanations from going on ad infinitum. Aristotle proceeds in the following two sections to criticize certain non-teleological accounts offered by his predecessors on the grounds that they are explanatorily defective: those accounts – unlike teleological explanations – neither begin from appropriate starting points nor entail the phenomena that they purport to explain. Along the way, the chapter proposes an alternative way to understand what “hypothetical necessity” refers to, for Aristotle.

Parts of Animals (PA) I.5 sends a strong message that the parts of the animal body are to be studied for the sake of the substance, the whole animal. If, as Aristotle suggests, it is the lowest or ‘indivisible’ species which are the substances, then we should study the parts of animals at this level. Yet many of the parts of animals are common to several species, so explaining them for each species would be repetitive and tiresome. We find thus in the PA two opposed explanatory tendencies: one ‘upwards’ toward the more common and greater simplicity and another ‘downwards’ toward the ultimate species and greater complexity. Aristotle’s proposed solution is to account for the various bodily parts at a general level and to descend to the species only when the parts differ significantly. In this chapter I discuss some difficulties for Aristotle’s solution.

This brief chapter considers what we mean by knowledge, explanation and understanding, aspects that have and remain areas of debate in the philosophy of science. Despite scientists referring to these aspects routinely in ways that suggest their meaning is clear, examples are given that suggest the terms can actually be used in various ways by different people. It is important to consider what is being claimed and why in a claimed explanation or a claim to understanding, because the terms carry different weights and subjectively mean different things. This can lead to confusion and errors of reasoning that can constrain a field.

This chapter looks at claims to understanding. It begins by looking at the system I have worked on, the lamprey spinal cord locomotor circuit, and claims that circuit function and behaviour can be understood in terms of the interactions of spinal cord nerve cells. I highlight that the claims to experimental confirmation actually reflect various assumptions and extrapolations and that the claimed understanding is lacking. I then look at the Nobel Prize winning work on the Aplysia gill withdrawal reflex, making the same conclusion as the lamprey, various assumptions and extrapolations are used to claim causal links, and in doing this commit various logical fallacies, including confusing correlation for causation and begging the question. I finish by looking at hippocampal long-term potentiation and claims it is the cellular basis of memory, again highlighting that the claimed links have not been made.

This chapter introduces the book’s project: relying extensively on the work of Alan Hodgkin and Alan Huxley on the confirmation of the role of sodium ions in generating the action potential, the book develops an account of singular compositional abduction. The first part of the book sets out the “science-first” methodological approach along with the positive theory of singular compositional abduction. The second part provides two cases studies of singular compositional abduction: The experimental work Hodgkin and Huxley used to support their theory of the action potential and some of the experimental work that has been brought to bear in attempts to understand the biological basis of the Hermann grid illusion. The third part contrasts the theory of singular compositional abduction with Peircean theories of abduction, Harman’s approach to inference to the best explanation, Lipton’s approach to inference to the best explanation, and the New Mechanist “matched interlevel experimental” approach.