An increasing number of arguments for causal pluralism invoke empirical psychological data. Different aspects of causal cognition—specifically, causal perception and causal inference—are thought to involve distinct cognitive processes and representations, and they thereby distinctively support transference and dependency theories of causation, respectively. We argue that this dualistic picture of causal concepts arises from methodological differences, rather than from an actual plurality of concepts. Hence, philosophical causal pluralism is not particularly supported by the empirical data. Serious engagement with cognitive science reveals that the connection between psychological concepts of causation and philosophical notions is substantially more complicated than is traditionally presumed.

This article presents a new argument for incorporating a distinction between default and deviant values into the formalism of causal models. The argument is based on considerations about how causal reasoners should represent disagreement over causes, and it is defended against an objection that has been raised against earlier arguments for defaults.

In the causal modeling literature, it is well known that ill-defined variables may give rise to ambiguous manipulations. Here, we illustrate how ill-defined variables may also induce mistakes in causal inference when standard causal search methods are applied. To address the problem, we introduce a representation framework, which exploits an independent component representation of the data, and demonstrate its potential for detecting ill-defined variables and avoiding mistaken causal inferences.

I propose a dynamic causal approach to characterizing the notion of a mechanism. Levy and Bechtel, among others, have pointed out several critical limitations of the new mechanical philosophy and pointed in a new direction to extend this philosophy. Nevertheless, they have not fully fleshed out what that extended philosophy would look like. Given a closer look at neuroscientific practice, I propose that a mechanism is a dynamic causal system that involves various components interacting, typically nonlinearly, with one another to produce a phenomenon of interest.

Reformulating a scientific theory often leads to a significantly different way of understanding the world. Nevertheless, accounts of both theoretical equivalence and scientific understanding have neglected this important aspect of scientific theorizing. This essay provides a positive account of how reformulation changes our understanding. My account simultaneously addresses a serious challenge facing existing accounts of scientific understanding. These accounts have failed to characterize understanding in a way that goes beyond the epistemology of scientific explanation. By focusing on cases in which we have differences in understanding without differences in explanation, I show that understanding does not reduce to explanation.

Epidemiological explanation often has a “black box” character, meaning the intermediate steps between cause and effect are unknown. Filling in black boxes is thought to improve causal inferences by making them intelligible. I argue that adding information about intermediate causes to a black box explanation is an unreliable guide to pragmatic intelligibility because it may mislead us about the stability of a cause. I diagnose a problem that I call wishful intelligibility, which occurs when scientists misjudge the limitations of certain features of an explanation. Wishful intelligibility gives us a new reason to prefer black box explanations in some contexts.

The coherence of independent reports provides a strong reason to believe that the reports are true. This plausible claim has come under attack from recent work in Bayesian epistemology. This work shows that, under certain probabilistic conditions, coherence cannot increase the probability of the target claim. These theorems are taken to demonstrate that epistemic coherentism is untenable. To date no one has investigated how these results bear on different conceptions of coherence. I investigate this situation using Thagard’s ECHO model of explanatory coherence. Thagard’s ECHO model provides a natural representation of the evidential significance of multiple independent reports.

This essay examines the provenance of a single, curious term that William James often used in connection with his own pragmatism. The term is Denkmittel, an uncommon German contraction of Denk (thought) and Mittel (instrument). James’s Central European sources for this now forgotten bit of philosophical jargon provide a small illustration of a bigger historical point that too often gets obscured. Pragmatism—James’s pragmatism, at least—was both allied with and inspired by a broader sweep of scientific instrumentalism that was already flourishing in fin de siècle European philosophy.

In this article I discuss various humility theses about individuals and intrinsic properties as discussed by authors such as David Lewis. I argue that we should accept a similar humility thesis about the world’s space-time structure regardless of which metaphysics of space-time we accept. I argue this undercuts some important motivations opting in for an ontic structural realist metaphysic.

Transcendental arguments are not popular in contemporary philosophy of science. They are typically seen as antinaturalistic and incapable of providing explanatory force in accounting for natural phenomena. However, when viewed as providing (certain types of) intelligibility to complicated concepts used in scientific reasoning, a concrete and productive role is recoverable for transcendental reasoning in philosophy of science. In this article I argue that the resources, and possibly the need, for such a role are available within a thoroughly naturalistic framework garnered from the work of Hasok Chang and William Wimsatt.

Adrian Currie has introduced a novel account of creativity within the social epistemology of science. The account is intended to capture how conservatism can be detrimental to the health of inquiry within certain scientific communities, given the aims of research there. I argue that recent remarks by Carlo Rovelli put pressure on the applicability of the account. Altogether, it seems we do not yet well understand the relationship between creativity, conservatism, and the health of inquiry in science.

According to the diversity-beats-ability theorem, groups of diverse problem solvers can outperform groups of high-ability problem solvers. We argue that the model introduced by Lu Hong and Scott Page is inadequate for exploring the trade-off between diversity and ability. This is because the model employs an impoverished implementation of the problem-solving task. We present a new version of the model that captures the role of ‘ability’ in a meaningful way, and we use it to explore the trade-offs between diversity and ability in scientific problem solving.

Philosophers of science and metascientists alike typically model scientists’ behavior as driven by credit maximization. In this article I argue that this modeling assumption cannot account for how scientists have a default level of trust in each other’s assertions. The normative implication of this is that science policy should not focus solely on incentive reform.

Normatively inappropriate scientific dissent prevents warranted closure of scientific controversies and confuses the public about the state of policy-relevant science, such as anthropogenic climate change. Against recent criticism by de Melo-Martín and Intemann of the viability of any conception of normatively inappropriate dissent, I identify three conditions for normatively inappropriate dissent: its generation process is politically illegitimate, it imposes an unjust distribution of inductive risks, and it adopts evidential thresholds outside an accepted range. I supplement these conditions with an inference-to-the-best-explanation account of knowledge-based consensus and dissent to allow policy makers to reliably identify unreliable scientific dissent.

There is a received view on measurement scales. It includes both a classification of scales (nominal, ordinal, interval, and ratio) and a set of prescriptions regarding measurement inferences. This article casts doubt on the adequacy of this received view. To do this, I propose an epistemic characterization of the ordinal/interval distinction, that is, one in terms of researchers’ beliefs. This novel characterization reveals the ordinal/interval distinction as too coarse grained and thus the received view as too restrictive of a framework for measurement research.

This article is about a start-up problem in scientific practice. Specifically, it is about the problem of justifying paleontological correlation—the practice of using fossils to establish time relations among fossiliferous rocks. Paleontological correlation was the key to assembling a geological timescale during the nineteenth century and remains an important practice in stratigraphic geology to this day. Yet contrary to philosophical expectations, this practice lacked a robust theoretical justification during the first half of the nineteenth century. This article examines what this lack of justification amounted to, as well as how the method of paleontological correlation was justified in practice.

Judging an act’s causal efficacy plays a crucial role in causal decision theory. A recent development appeals to the causal modeling framework with an emphasis on the analysis of intervention based on the causal Bayes net for clarifying what causally depends on our acts. However, few writers have focused on exploring the usefulness of extending structural causal models to decision problems that are not ideal for intervention analysis. The thesis concludes that structural models provide a more general framework for rational decision makers.

Dutch Book Arguments (DBAs) have been invoked to support various alleged requirements of rationality. Some alleged requirements, such as probabilistic coherence and conditionalization, are plausible. Others, such as credal transparency and reflection, may be less so. Anna Mahtani has argued for a new understanding of DBAs that, she claims, allows us to keep the DBAs for the plausible requirements while rejecting those for the implausible ones. I argue that Mahtani’s new account fails, as (a) it does not support highly plausible requirements of rational coherence and (b) it does not succeed in undermining the DBAs for credal transparency or reflection.

The Borel-Kolmogorov paradox is often presented as an obscure problem that certain mathematical accounts of conditional probability must face. In this article, we point out that the paradox arises in the physical sciences, for physical probability or chance. By carefully formulating the paradox in this setting, we show that it is a puzzle for everyone, regardless of one’s preferred probability formalism. We propose a treatment that is inspired by the approach that scientists took when confronted with these cases.