While projects in philosophy of science often focus on similar sorts of topics, there are often massive differences in how philosophers approach them. One approach that has enjoyed significant success—and that is modeled in the work of Wimsatt and others—involves taking a conceptual engineering perspective in analyzing these topics. In this paper, we outline main features of this conceptual engineering approach and use it to advance three emerging claims regarding causal explanation in biology and neuroscience. We suggest that causal explanation in these domains is non-reductive in contrast to always requiring (or improving with) lower-level details, that it is guided by precise explanatory targets in contrast to complete or multidimensional explanatory targets, and that it is pluralist in involving importantly distinct types of causal systems.

Abstract This paper argues that when brain networks figure in explanations of cognition and behavior they often do so in conjunction with independent dynamical assumptions about signal transmission in the brain. In such cases explanation does not depend on network structure alone but on network structure operating in conjunction with dynamical assumptions. Moreover, dynamical assumptions embody causal information, so that the resulting explanations are not entirely non-causal. In addition, it is argued that the directional features of explanations that appeal to networks can be understood in terms of the independence of network structure and dynamics.

According to mainstream philosophical views causal explanation in biology and neuroscience is mechanistic. As the term “mechanism” gets regular use in these fields it is unsurprising that philosophers consider it important to scientific explanation. What is surprising is that they consider it the only causal term of importance. This paper provides an analysis of a new causal concept–it examines the cascade concept in science and the causal structure it refers to. I argue that this concept is importantly different from the notion of mechanism and that this difference matters for our understanding of causation and explanation in science. This paper provides an analysis of the cascade concept in science and the causal structure it refers to. 2 I examine the main features of this causal structure, analogies it is associated with, and strategies used to study it. While scientific work supports distinguishing the cascade and mechanism concepts, this analysis is not merely descriptive. Instead, it provides a theoretical framework for how these concepts should be understood. This framework matters for our assessment of the causal structure of the world, how we study this structure, use it to produce particular outcomes, and communicate about it to others. Before proceeding with this analysis, two clarifications are in order. First, I do not suggest that scientists always use these causal terms in the distinct ways indicated in this analysis, but that they often do and should use them in this way. This reveals normative features of this work and an important way that philosophy can contribute to science, namely, by making suggestions for how these concepts should be understood and used. Second, my analysis of these concepts articulates clear ways in which they differ, but leaves space for some structures in science to be borderline. The presence of such cases should not prevent us from articulating useful categories that distinguish causal structures in the majority of cases, even if the distinction can (in rare cases) be blurred.

Although the brain is often characterized as a complex system, theoretical and philosophical frameworks often struggle to capture this. For example, mainstream mechanistic accounts model neural systems as fixed and static in ways that fail to capture their dynamic nature and large set of possible behaviors. In this paper, we provide a framework for capturing a common type of complex system in neuroscience, which involves two main aspects: (i) constraints on the system and (ii) the system's possibility space of available outcomes. Our analysis merges neuroscience examples with recent work in the philosophy of science to suggest that the possibility space concept involves two essential types of constraints, which we call hard and soft constraints. Our analysis focuses on a domain-general notion of possibility space that is present in manifold frameworks and representations, phase space diagrams in dynamical systems theory, and paradigmatic cases, such as Waddington's epigenetic landscape model. After building the framework with such cases, we apply it to three main examples in neuroscience: adaptability, resilience, and phenomenology. We explore how this framework supports a philosophical toolkit for neuroscience and how it helps advance recent work in the philosophy of science on constraints, scientific explanations, and impossibility explanations. We show how fruitful connections between neuroscience and philosophy can support conceptual clarity, theoretical advances, and the identification of similar systems across different domains in neuroscience.

This Element examines philosophical accounts of scientific explanation, particularly those that apply to biology and the life sciences. Two main categories of scientific explanation are examined in detail –causal explanations and non-causal explanations. The first section of this Element provides a brief history and some basics on philosophical accounts of scientific explanation. Section 2 covers causal explanation, first by discussing foundational topics in the area, such as defining causation, causal selection, and reductive explanation. This is followed by an examination of distinct types of causal explanation, including those that appeal to mechanisms pathways, and cascades. The third section covers non-causal, mathematical explanations, which have received significant attention in philosophy of biology and the life sciences. Three main types of non-causal, mathematical explanation are discussed: topological and constraint-based explanation, optimality and efficiency explanations, and minimal model explanations. This title is also available as Open Access on Cambridge Core.

The preference for simple explanations, known as the parsimony principle, has long guided the development of scientific theories, hypotheses, and models. Yet recent years have seen a number of successes in employing highly complex models for scientific inquiry (e.g., for 3D protein folding or climate forecasting). In this paper, we reexamine the parsimony principle in light of these scientific and technological advancements. We review recent developments, including the surprising benefits of modeling with more parameters than data, the increasing appreciation of the context-sensitivity of data and misspecification of scientific models, and the development of new modeling tools. By integrating these insights, we reassess the utility of parsimony as a proxy for desirable model traits, such as predictive accuracy, interpretability, effectiveness in guiding new research, and resource efficiency. We conclude that more complex models are sometimes essential for scientific progress, and discuss the ways in which parsimony and complexity can play complementary roles in scientific modeling practice.

A fundamental goal of research in neuroscience is to uncover the causal structure of the brain. This focus on causation makes sense, because causal information can provide explanations of brain function and identify reliable targets with which to understand cognitive function and prevent or change neurological conditions and psychiatric disorders. In this research, one of the most frequently used causal concepts is 'mechanism' - this is seen in the literature and language of the field, in grant and funding inquiries that specify what research is supported, and in journal guidelines on which contributions are considered for publication. In these contexts, mechanisms are commonly tied to expressions of the main aims of the field and cited as the 'fundamental', 'foundational' and/or 'basic' unit for understanding the brain. Despite its common usage and perceived importance, mechanism is used in different ways that are rarely distinguished. Given that this concept is defined in different ways throughout the field - and that there is often no clarification of which definition is intended - there remains a marked ambiguity about the fundamental goals, orientation and principles of the field. Here we provide an overview of causation and mechanism from the perspectives of neuroscience and philosophy of science, in order to address these challenges. 'Mechanism' is a frequently used causal concept in neuroscience but can have different meanings that are often not specified. In this Review, Ross and Bassett explore these different meanings and the challenges associated with the variable usage of this term before discussing how these challenges may be met.

This paper examines constraints and their role in scientific explanation. Common views in the philosophical literature suggest that constraints are non-causal and that they provide non-causal explanations. While much of this work focuses on examples from physics, this paper explores constraints from other fields, including neuroscience, physiology, and the social sciences. I argue that these cases involve constraints that are causal and that provide a unique type of causal explanation. This paper clarifies what it means for a factor to be a constraint, when such constraints are causal, and how they figure in scientific explanation.

Social scientists appeal to various “structures” in their explanations including public policies, economic systems, and social hierarchies. Significant debate surrounds the explanatory relevance of these factors for various outcomes such as health, behavioral, and economic patterns. This paper provides a causal account of social structural explanation that is motivated by Haslanger (2016). This account suggests that social structure can be explanatory in virtue of operating as a causal constraint, which is a causal factor with unique characteristics. A novel causal framework is provided for understanding these explanations–this framework addresses puzzles regarding the mysterious causal influence of social structure, how to understand its relation to individual choice, and what makes it the main explanatory (and causally responsible) factor for various outcomes.

This paper provides an analysis of explanatory constraints and their role in scientific explanation. This analysis clarifies main characteristics of explanatory constraints, ways in which they differ from “standard” explanatory factors, and the unique roles they play in scientific explanation. While current philosophical work appreciates two main types of explanatory constraints, this paper suggests a new taxonomy: law-based constraints, mathematical constraints, and causal constraints. This classification helps capture unique features of constraint types, the different roles they play in explanation, and it includes causal constraints, which are often overlooked in this literature.

We explore Madole & Harden's (2022) suggestion that single-nucleotide polymorphism (SNP)/trait correlations are analogous to randomized experiments and thus can be given a causal interpretation.

Recent philosophical work on causation has focused on distinctions across types of causal relationships. This paper argues for another distinction that has yet to receive attention in this work. This distinction has to do with whether causal relationships have “material continuity,” which refers to the reliable movement of material from cause to effect. This paper provides an analysis of material continuity and argues that causal relationships with this feature (1) are associated with a unique explanatory perspective, (2) are studied with distinct causal investigative methods, and (3) provide different types of causal control over their effects.

In neuroscience, the term 'causality' is used to refer to different concepts, leading to confusion. Here we illustrate some of those variations, and we suggest names for them. We then introduce four ways to enhance clarity around causality in neuroscience.

This paper explores a distinction among causal relationships that has yet to receive attention in the philosophical literature, namely, whether causal relationships are reversible or irreversible. We provide an analysis of this distinction and show how it has important implications for causal inference and modeling. This work also clarifies how various familiar puzzles involving preemption and over-determination play out differently depending on whether the causation involved is reversible.

Over the last two decades few topics in philosophy of science have received as much attention as mechanistic explanation. A significant motivation for these accounts is that scientists frequently use the term 'mechanism' in their explanations of biological phenomena. Of course, biologists use a variety of causal concepts in their explanations, including concepts like pathways, cascades, triggers, and processes. Despite this variety, mainstream philosophical views interpret all of these concepts with the single notion of mechanism. In using the mechanism concept interchangeably with other causal concepts, it is not clear that these accounts well capture the diversity of causal structures in biology. This article analyses two causal concepts in biology-the notions of 'mechanism' and 'pathway'-and how they figure in biological explanation. I argue that these concepts have unique features, that they are associated with distinct strategies of causal investigation, and that they figure in importantly different types of explanation.

This paper examines tracer techniques in neuroscience, which are used to identify neural connections in the brain and nervous system. These connections capture a type of “structural connectivity” that is expected to inform our understanding of the functional nature of these tissues (Sporns in Scholarpedia, 2007). This is due to the fact that neural connectivity constrains the flow of signal propagation, which is a type of causal process in neurons. This work explores how tracers are used to identify causal information, what standards they are expected to meet, the forms of causal information they provide, and how an analysis of these techniques contributes to the philosophical literature, in particular, the literature on mark transmission and mechanistic accounts of causation.

This article examines the multiple realizability thesis within a causal framework. The beginnings of this framework are found in Elliott Sober’s “Multiple Realizability Argument against Reduction,” which argues that the multiple realizability thesis poses no challenge to reductive explanation. While Sober’s causal approach has the potential to reveal new insights, I argue that his setup fails to capture important aspects of the multiple realizability thesis. After correcting for these issues, I argue that this causal framework reveals something quite different. It reveals how multiple realizability relates to a common type of causal complexity in biology that poses problems for reductive explanation.