Causal Reasoning

When two causally independent processes each have a quantity that increases monotonically (either deterministically or in probabilistic expectation), the two quantities will be correlated, thus providing a counterexample to Reichenbach's principle of the common cause. Several philosophers have denied this, but I argue that their efforts to save the principle are unsuccessful. Still, one salvage attempt does suggest a weaker principle that avoids the initial counterexample. However, even this weakened principle is mistaken, as can be seen by exploring the concepts (...) of homology and homoplasy used in evolutionary biology. I argue that the kernel of truth in the principle of the common cause is to be found by separating metaphysical and epistemological issues; as far as the epistemology is concerned, the Likelihood Principle is central. (shrink)

In his Examination of Relations (Sambandhaparīkṣā), Dharmakīrti rejects the existence of mind-independent relations. Instead, on his view, all relations are merely conceptual constructs. While this is a significant conclusion on its own, several of Dharmakīrti’s Indian and Tibetan commentators argue that his denial of real relations has far-reaching implications for the ontological status of everything that we generally take to populate the world—both persons and ordinary objects. This paper focuses on the case of persons. After providing an overview of Dharmakīrti’s (...) own view on the status of persons and relations, I show how three of his commentators rely on the rejection of a different kind of relation to rule out the possibility of an autonomous self: Śaṅkaranandana’s argument turns on rejecting an inherence relation, Chomden Reldri’s on an agential relation, and Gyaltsab Darma Rinchen’s on an autonomy relation. I develop an analysis of Gyaltsab’s apparently novel proposal of an autonomy relation, and I argue that, of these three commentators, Gyaltsab’s argument looks the most promising. I conclude by considering how the claims of these commentators may be reconciled with the prima facie incompatible conclusions of Dharmakīrti himself concerning the nature of things. (shrink)

Quantitative intelligence analysis often leans on pattern recognition, but in adversary‑shaped environments correlations can be engineered. Building on Judea Pearl’s structural causal models, this article is makes identification, not estimation, the gate to credible claims. It shows when effects are recoverable from observational data and when leverage—mediators, instruments, modest interventions, or transport adjustments—is required. The framework unifies association, intervention, and counterfactuals and extends to sequential and multi‑agent settings (Elias Bareinboim’s causal reinforcement learning and causal game theory), where strategies are modelled (...) as interventions and evaluated under adaptation. Across operational cases, analysts specify a causal graph, test its implications, determine identifiability, and only then estimate. This identification‑first discipline separates artefacts from effects, hardens AI/ML pipelines against manipulation, and ties analysis directly to ‘decision advantage’: identify → estimate → decide → iterate. The result is an explicit, testable mapping from action to outcome that turns uncertain signals into reliable policy support. Echoing Sherman Kent, intelligence analysis earns trust when it distinguishes observation from inference and states what is known—and unknown—up front. (shrink)

This thesis presents Fluctuational Logic (FL), a novel logical framework designed to address the fundamental inadequacies of classical and existing non-classical logic systems when reasoning about emergent phenomena where identity, persistence, and causality are not fixed properties but emerge through dynamic processes. Through comprehensive analysis of existing approaches including classical logic, quantum logic, temporal logic, dynamic epistemic logic, paraconsistent logic, and process philosophy, this work identifies critical limitations in their capacity to model emergence adequately. -/- FL introduces the Fluctuation Principle, (...) which recognizes that truth conditions themselves undergo continuous fluctuation as emergent processes evolve. The framework employs Emergent Identity Operators (≈ₛ, ≈ₑ, ≈φ), Fluctuational Causality relations (⟹φ, ⟹?, ⟹∅), and Temporal Emergence Operators (◊ₑ, □φ, Uₑ) to capture the dynamic nature of emergent reality. The formal system includes a complete axiomatization, semantic framework based on Fluctuational Models, and inference rules specifically designed for emergent phenomena. -/- Critical comparative analysis demonstrates FL's distinctive advantages: ontological flexibility that adapts to emergent phenomena, integrated temporal-causal framework, active contradiction resolution mechanisms, and scalable complexity management. Applications to consciousness studies, complex systems modeling, and quantum emergence phenomena validate the framework's practical utility. The thesis concludes that FL provides the first comprehensive logical framework adequate for reasoning about reality where fundamental properties emerge rather than exist as fixed features. (shrink)

Contemporary philosophy of science analyzes psychology as a science with special features, because this discipline includes some specific philosophical problems – descriptive and normative, structural and dynamic. Some of these are particularly relevant both theoretically and practically. Two central aspects in this book are the role of causality, especially conceived as intervention or manipulation, and the characterization of the psychological subject. This requires a clarification of scientific explanations in terms of causality in psychology, because characterizations of causality are quite different (...) in epistemological and ontological terms. One of the most influential views is James Woodward’s approach to causality as intervention, which entails an analysis of its characteristics, new elements and limits. This means taking into account the structural and dynamic aspects included in causal cognition and psychological explanations. Psychology seen as special science also requires us to consider the scientific status of psychology and the psychological subject, which leads to limits of naturalism in psychology. (shrink)

Judea Pearl’s causal formalism marked a paradigm shift in scientific reasoning by elevating causation from correlation through graphical models and symbolic intervention calculus. Yet, under the lens of Recursive Coherence, his framework reveals critical structural deficiencies. This paper deconstructs Pearl’s causal logic through the five axioms of recursive coherence reasoning (persistence, trajectory, information value, arbitration, and pruning) and identifies key blind spots: static graph assumptions, binary intervention logic, fragile counterfactuals, unfiltered signal inflation, and identifiability-based arbitration. These weaknesses lead to coherence (...) stagnation, entropy retention, and reduced option space across dynamic systems. I then reconstruct a coherence-maximizing alternative: dynamic causal topologies derived from smoothed coherence gradients, gradient-based interventions replacing binary “do” operations, μ-validated signal filtration, and Δ²C-optimized path arbitration. The resulting system retains Pearl’s explanatory power while restoring adaptive viability, maximizing structural alignment under entropy descent. This reframing challenges the sufficiency of identifiability as the core criterion for causal adequacy and proposes coherence curvature as the new metric for scientific reasoning. Pearl’s insights are thus not discarded but refactored; his revolution, now recursive. (shrink)

The empirical and theoretical resources of different disciplines are often combined to shed light on questions that concern the deep history of humanity, such as the geographic origin of people groups, patterns of migration, and the diffusion of culture. In this article, I discuss three ways in which other disciplines, such as biology and archaeology, are integrated with historical linguistics to enhance our understanding of the past. First, other disciplines provide background knowledge that helps to constrain and assess competing historical (...) explanations of the data. I examine this issue in the context of the Principle of the Common Cause and Inference to the Best Explanation. Second, the synthesis of data from multiple disciplines provides a diverse and varied set of evidence, which is epistemically superior, other things being equal, to a homogeneous set of evidence. I consider several justifications for the confirmational value of diverse evidence. Finally, methods and procedures from other disciplines can often be fruitfully applied to forge new pathways to knowledge. Here, I survey the recent application of phylogenetic methods from biology, used for inferring evolutionary trees, within historical linguistics. (shrink)

Consider the problem of learning, from non-experimental data, the causal (Markov equivalence) structure of the true, unknown causal Bayesian network (CBN) on a given, fixed set of (categorical) variables. This learning problem is known to be very hard, so much so that there is no learning algorithm that converges to the truth for all possible CBNs (on the given set of variables). So the convergence property has to be sacrificed for some CBNs—but for which? In response, the standard practice has (...) been to design and employ learning algorithms that secure the convergence property for at least all the CBNs that satisfy the famous faithfulness condition, which implies sacrificing the convergence property for some CBNs that violate the faithfulness condition (Spirtes, Glymour, and Scheines, 2000). This standard design practice can be justified by assuming—that is, accepting on faith—that the true, unknown CBN satisfies the faithfulness condition. But the real question is this: Is it possible to explain, without assuming the faithfulness condition or any of its weaker variants, why it is mandatory rather than optional to follow the standard design practice? This paper aims to answer the above question in the affirmative. We first define an array of modes of convergence to the truth as desiderata that might or might not be achieved by a causal learning algorithm. Those modes of convergence concern (i) how pervasive the domain of convergence is on the space of all possible CBNs and (ii) how uniformly the convergence happens. Then we prove a result to the following effect: for any learning algorithm that tackles the causal learning problem in question, if it achieves the best achievable mode of convergence (considered in this paper), then it must follow the standard design practice of converging to the truth for at least all CBNs that satisfy the faithfulness condition—it is a requirement, not an option. (shrink)

What are the roles of scientific causal knowledge in relation to the evidential requirements of policy making? In this chapter, I review the existing approaches in philosophy of science to the policy relevance of causal knowledge. I assess the specific concerns and questions on which these philosophical accounts have focused and show how they only offer a partial perspective of the relation between causal knowledge and policy making. Most existing views are top-down approaches: they start from philosophical concerns about causation (...) and evidence, and then make general claims about (potential) policy implications, but leave the notion of “policy” unanalyzed. As an alternative, I put forward some ideas toward a bottom-up approach: one that understands policy making as a dynamic process with several stages, and that starts from concrete policy problems, to then examine how causal notions and methods can contribute in various ways to the specifics of the process of policy making. (shrink)

Experiments in psychology and experimental philosophy suggest that judgments about actual causation are partially governed by norms: norm violations are more likely to be singled out as causes, while structurally analogous factors that obey the norms are unlikely to be singled out. The norm-sensitivity of causal judgment has, in turn, lent support to a normative analysis of causation itself. In this paper, I question whether the support stands. I articulate and examine two principal reasons support might be so derived. For (...) each, I argue that, in fact, a non-normative analysis is better supported. (shrink)

Understanding causation in social-ecological systems (SES) is indispensable for promoting sustainable outcomes. However, the study of such causal relations is challenging because they are often complex and intertwined, and their analysis involves diverse disciplines. Although there is agreement that no single research approach (RA) can comprehensively explain SES phenomena, there is a lack of ability to deal with this diversity. Underlying this diversity and the challenge of dealing with it are different causal reasonings that are rarely explicit. Awareness of hidden (...) assumptions is essential for understanding how the causal reasoning of an RA is constituted, and for promoting the integration, translation, or juxtaposition of different RAs. We identify the following elements as particularly relevant for understanding causal reasoning: methods, frameworks and theories, accounts of causation, analytical focus, and causal notions. We begin with the idea that one of these elements typically figures as an entry point to an RA. This entry point is particularly important because it generates a path dependence that orients causal reasoning. In a subsequent step, when an approach is applied, causal reasoning concretizes as a result of a particular constellation of the remaining elements. We come to these insights by studying the application of four different RAs to the same social-ecological case (the collapse of Baltic cod stocks in the 1980s). On the basis of our findings we developed a guide for the analysis of causal reasoning by raising awareness of the assumptions, key elements, and the relations between these key elements for a given RA. The guide can be used to elicit the causal reasoning of RAs, facilitate interdisciplinary collaboration, and support disclosure of ethical/political dimensions that underlie management/governance interventions that are formulated on the basis of causal findings of research studies. (shrink)

“Excited Delirium Syndrome” (ExDS) is a controversial diagnosis. The supposed syndrome is sometimes considered to be a potential cause of death. However, it has been argued that its sole purpose is to cover up excessive police violence because it is mainly used to explain deaths of individuals in custody. In this paper, we examine the epistemic conditions giving rise to the controversial diagnosis by discussing the relation between causal hypotheses, evidence, and data in forensic medicine. We argue that the practitioners’ (...) social context affects causal inquiry through background assumptions that enter inquiry at multiple stages. This analysis serves to better understand the wide usage of the controversial diagnosis of ExDS. (shrink)

A formal theory of causal reasoning is presented that encompasses both Pearl’s approach to causality and several key formalisms of nonmonotonic reasoning in Artificial Intelligence. This theory will be derived from a single rationality principle of causal acceptance for propositions. However, this principle will also set the theory of causal reasoning apart from common representational approaches to reasoning formalisms.

While loneliness has been linked to various mental and physical health problems, the sense in which loneliness is a cause of these conditions has so far attracted little philosophical attention. This paper aims to fill this gap by analyzing research on health effects of loneliness and therapeutic interventions through current approaches to causality. To deal with the problem of causality between psychological, social, and biological variables, the paper endorses a biopsychosocial model of health and disease. I will investigate how three (...) main approaches to causality used in psychiatry and public health apply to loneliness: interventionism, mechanisms, and dispositional theories. Interventionism can specify whether loneliness causes specific effects, or whether a treatment works, incorporating results from randomized controlled trials. Mechanisms help explain how loneliness brings about negative health effects, spelling out psychological processes involved in lonely social cognition. Dispositional approaches help stress particular features of loneliness connected to negative social interactions, such as defensiveness. I will conclude by showing that previous research alongside emerging approaches to health effects of loneliness lend themselves to analysis in terms of the causal models under discussion. (shrink)

This paper argues that according to Adam Smith the workings of (anything but extremely simple) sympathetic judgment (s) presuppose and crucially depend on counterfactual causal reasoning in the sympathetic process. In particular it argues for four related claims: (i) that according to Smith that the sympathetic process depends on a type of causal reasoning that goes well beyond the kind of simulationist theory standardly attributed to him; (ii) that the Smithian imagination in the sympathetic process works by way of counterfactual (...) reasoning and that even the feelings we ought to feel as a consequence of the sympathetic process need not be actual, but counterfactual; (iii) that Smithian agents are non-trivially understood as belonging to the causal order of nature; (iv) that Smitian judgments of propriety are intrinsically judgments about the proportionality of causal relations. (shrink)

This paper proposes a new metaphysical framework for distinguishing between human and machine intelligence by drawing on Kant’s incongruent counterparts as an analogy. Specifically, the paper posits two deterministic worlds that are superficially identical but ultimately different. Using ideas from Wittgenstein, Gödel, and Cantor, the paper defines “deterministic knowledge” and investigates how this knowledge is processed differently in those two worlds. The paper considers computationalism and causal determinism for the new framework. Then, the paper introduces new concepts to illustrate why (...) human and machine agents display different causal characteristics in processing verbal information. Overall, the paper’s framework provides a theoretical basis for the uniqueness of the human mind. (shrink)

Philosophical analyses of causation have been centred on the question of what causation is. More precisely speaking, philosophers tend to address four different issues: metaphysical (what is causation out there?), epistemological (how can a causal claim be established and assessed?), conceptual (what does the word ‘cause’ mean?), and methodological (what methods ought one to use in order to establish and assess causal claims?). This chapter argues that the practical issue of causation (what is a causal claim for in practice?) is (...) highly relevant to the metaphysical, epistemological, conceptual, and methodological issues of causation. Any philosophical enquiry into causation without an examination of this practical issue is incomplete. In other words, a promising approach to causation ought to take all of the metaphysical, epistemological, conceptual, methodological, and practical issues into account. (shrink)

Causal cognition – how we perceive, represent and reason about causal events – are fundamental to the human mind, but it has rarely been approached in its cultural specificity. Here, we investigate this core concept among Wichi people, an indigenous group living in Chaco Forest. We focus on the Wichi, because their epistemological orientations and explanatory frameworks about ecosystem differ importantly from those documented among most Western majority-culture populations. We asked participants to reason about causes of events that involve the (...) hunhat lheley (inhabitants of the earth: humans, non-human animals, plants and spiritual beings) and other entities of their ecosystem (e.g., lagoon). We find a native ontological framework that encompasses three interacting organizing principles. This new evidence highlights ways in which native categories guide causal reasoning. Our research challenge long-held assumptions that dichotomies – nature-culture or natural-supernatural – are universal features of the human mind. (shrink)

A novel attempt to explain why teens and adults often struggle with scientific explanation even thought young children clearly possess impressive causal reasoning skills.

Cause and effect relationships play a central role in how we perceive and make sense of the world around us, how we act upon it, and ultimately, how we understand ourselves. Almost two decades ago, computer scientist Judea Pearl made a breakthrough in understanding causality by discovering and systematically studying the “Ladder of Causation” [Pearl and Mackenzie 2018], a framework that highlights the distinct roles of seeing, doing, and imagining. In honor of this landmark discovery, we name this the Pearl (...) Causal Hierarchy (PCH). In this chapter, we develop a novel and comprehensive treatment of the PCH through two complementary lenses, one logical-probabilistic and another inferential-graphical. Following Pearl’s own presentation of the hierarchy, we begin by showing how the PCH organically emerges from a well-specified collection of causal mechanisms (a structural causal model, or SCM). We then turn to the logical lens. Our first result, the Causal Hierarchy Theorem (CHT), demonstrates that the three layers of the hierarchy almost always separate in a measure theoretic sense. Roughly speaking, the CHT says that data at one layer virtually always underdetermines information at higher layers. Since in most practical settings the scientist does not have access to the precise form of the underlying causal mechanisms – only to data generated by them with respect to some of PCH’s layers – this motivates us to study inferences within the PCH through the graphical lens. Specifically, we explore a set of methods known as causal inference that enable inferences bridging PCH’s layers given a partial specification of the SCM. For instance, one may want to infer what would happen had an intervention been performed in the environment (second-layer statement) when only passive observations (first-layer data) are available. We introduce a family of graphical models that allows the scientist to represent such a partial specification of the SCM in a cognitively meaningful and parsimonious way. Finally, we investigate an inferential system known as docalculus, showing how it can be sufficient, and in many cases necessary, to allow inferences across PCH’s layers. We believe that connecting with the essential dimensions of human experience as delineated by the PCH is a critical step towards creating the next generation of AI systems that will be safe, robust, human-compatible, and aligned with the social good. (shrink)

I provide an overview and critical discussion of Causation with a Human Face, by James Woodward.

This paper concerns the recent revival of entity realism. Having been started with the work of Ian Hacking, Nancy Cartwright and Ronald Giere, the project of entity realism has recently been developed by Matthias Egg, Markus Eronen, and Bence Nanay. The paper opens a dialogue among these recent views on entity realism and integrates them into a more advanced view. The result is an epistemological criterion for reality: the property-tokens of a certain type may be taken as real insofar as (...) only they can be materially inferred from the evidence obtained in a variety of independent ways of detection. (shrink)

Habilitation thesis in psychology. The book consists of a collection of reasoning studies. The experimental investigations will take us from people’s reasoning about probabilities, entailments, pragmatic factors, argumentation, and causality to morality. An overarching theme of the book is norm pluralism and individual differences in rationality research.

Formalizing commonsense knowledge for reasoning about time has long been a central issue in AI. It has been recognized that the existing formalisms do not provide satisfactory solutions to some fundamental problems, viz. the frame problem. Moreover, it has turned out that the inferences drawn do not always coincide with those one had intended when one wrote the axioms. These issues call for a well-defined formalism and useful computational utilities for reasoning about time and change. Yoav Shoham of Stanford University (...) introduced in his 1986 Yale doctoral thesis an appealing temporal nonmonotonic logic and identified a class of theories, causal theories, which have computationally simple model-theoretic properties. This paper is a study towards building upon Shoham's work on causal theories. We concentrate on improving computational aspects of causal theories while preserving their model-theoretic properties. (shrink)

We extend causal theories and study actions in domains involving multiple agents. Causal theories, invented by Yoav Shoham, are based on a temporal nonmonotonic logic and have computationally tractable aspects. Since Shoham's formalism does not provide an adequate mechanism for representing simultaneous actions and specifying their consequences, we introduce the notion of counteractions while preserving the efficiency and model-theoretic properties of causal theories.

This paper aims to scrutinize how the notion of risk should be understood and applied to possibly catastrophic cases. I begin with clarifying the standard usage of the notion of risk, particularly emphasizing the conceptual relation between risk and probability. Then, I investigate how to make decisions in the case of seemingly catastrophic disasters by contrasting the precautionary principle with the preventive (prevention) principle. Finally, I examine what kind of causal thinking tends to be actually adopted when we make decisions (...) on emergent cases. My arguments are mainly based on Japan's 2011 massive earthquake and nuclear power station accident. Masaki Ichinose. (shrink)

This research topic will review and further explore the nature of the mutual influence between time and causality, how causal knowledge is constructed in the context of time, and how it in turn shapes and alters our perception of time. We aim to draw together literatures from the perception and cognitive science, and welcome experimental as well as theoretical papers. Contributions investigating the neural bases of binding and causal learning/perception, methodological advances, as well as articles addressing functional implications of causal (...) learning and perception in real time are also welcome. (shrink)

Causal pluralism can be defended not only in respect to causal concepts and methodological guidelines, but also at the finer-grained level of causal inference from a particular source of evidence for causation. An argument for this last variety of pluralism is made based on an analysis of causal inference from randomized experiments (RCTs). Here, the causal interpretation of a statistically significant association can be established via multiple paths of reasoning, each relying on different assumptions and providing distinct elements of information (...) in favour of a causal interpretation. (shrink)

Aristotelian causal theories incorporate some philosophically important features of the concept of cause, including necessity and essential character. The proposed formalization is restricted to one-place predicates and a finite domain of attributes (without individuals). Semantics is based on a labeled tree structure, with truth defined by means of tree paths. A relatively simple causal prefixing mechanism is defined, by means of which causes of propositions and reasoning with causes are made explicit. The distinction of causal and factual explanation are elaborated, (...) and examples of cyclic and convergent causation are given. Soundness and completeness proofs are sketched. (shrink)

This article explores the relationship between graded, probabilistic representations of partial belief (i.e., credences) and binary representations of outright belief. It is often argued that outright beliefs simplify our credal reasoning. However, Staffel (2019) complicates this picture by arguing that if outright beliefs help us to update our credences through a process called pseudo-conditionalization, then beliefs do not actually simplify credal reasoning. This is because Staffel's model of pseudo-conditionalization is just as computationally intractable as the exact Bayesian process of credal (...) updating via conditionalization. I put forward a different model of pseudo-conditionalization that is computationally tractable. My model depends crucially on the existence of outright beliefs whose objects are not individual propositions but are instead probabilistic influence relations between propositions. Thus, I provide a new account of the belief-credence relation, according to which beliefs simplify our credal reasoning via a process of pseudo-conditionalization. (shrink)

Past studies have shown that while causal Bayes Nets account for many of the causal inferences participants make in psychological experiments, persistent violations of one of their most fundamental axioms, the causal Markov condition, are found. Previous studies have attempted to account for such violations by conjecturing that participants posit causal representations that diverge from the causal representations intended by the experimenters. In this paper, a novel method is presented for addressing the unsolved problem of how to determine which causal (...) representations participants apply in a causal inference problem and it is investigated how they update these causal representations when information about the underlying mechanism is given. The experiments thereby systematically investigate how participants represent causal structures and integrate mechanistic information to examine whether assumptions about hidden mechanisms stemming from these sources can account for participants’ Markov violations. Through these experiments, it is found that participants’ Markov violations can neither be accounted for through hidden variables that do not entail the conditional independence relations investigated nor through missing knowledge about underlying causal mechanisms. Instead, they indicate a persistent reasoning error which is shown to be robust to variations in task formats but which can be reduced under certain circumstances. (shrink)

Science likes to pretend it grew out of myth. The lab coat, p-value, and randomized controlled trials are all presented as a decisive break from the priesthoods and oracles of earlier eras. But when you look closely at how causal claims are manufactured and sold, you see something far less flattering. You see a priesthood with better notation. You see ritualized procedures for turning uncertainty into pronouncement. You see in Judea Pearl’s causal framework not just a technical achievement, but a (...) ready-made liturgy for a culture that would rather sound certain than be structurally aligned with reality. The danger is not that Pearl made causal reasoning computable. The danger is that his formalism arrived in a scientific ecosystem already addicted to certainty, and it became another instrument for performing knowledge rather than interrogating it. (shrink)

Suppositions can be classified as indicative vs. subjunctive and full vs. partial. We propose a causal account of suppositional reasoning that naturally unifies all four types of reasoning based on this classification, provides a justification of the rather heterogenous canonical update rules, and gives rise to a new update rule for the partial subjunctive case in terms of generalized imaging.

What does it mean to understand—and can machines do it? This paper presents a philosophical account of understanding and what it means to demonstrate understanding. The ways in which machines demonstrate understanding is then explored through the lens of modern machine learning practices. Understanding is defined as an internal model of causal relationships, and I argue that it is evidenced by the ability to generalize to novel problems. To distinguish true understanding from rote memorization, I introduce the recall machine as (...) a theoretical null hypothesis—a system that can mimic surface-level competence without internalizing structure. Using this framework, I analyze machine learning systems and show that many common evaluation techniques (e.g. out-of-distribution validation) implicitly test for understanding. Ultimately, I argue that machines can and do exhibit domain-specific forms of understanding, and I propose a new outlook for evaluating that capacity across systems. (shrink)

We offer a category-theoretic representation of the process theory of causality. The new formalism allows process theorists to (i) explicate their explanatory strategies (etiological and constitutive explanations) using the compositional features of string diagrams; (ii) probabilistically evaluate causal effects through the categorical notion of functor; (iii) address the problem of explanatory irrelevance via diagram surgery; and (iv) provide a theoretical explanation for the difference between conjunctive and interactive forks. We also claim that the fundamental building blocks of the process theory—namely (...) processes, interactions, and events—can be modeled using three types of morphisms. Overall, categorical modeling demonstrates that the philosophical theory of process causality possesses scientific rigor and expressive power comparable to those of its event-based counterparts, such as causal Bayes nets. (shrink)

In “Factual Difference-Making,” Andreas and Günther articulate and defend a novel analysis of actual causation. However, while both careful and interesting, I question what progress it really makes. I focus on two claims to progress: that factual difference-making (i) is complete; and (ii) need not invoke miracles. I argue that, ultimately, factual difference-making faces the same challenge as other analyses. I also suggest that the answer is to embrace a kind of causal relativism. The remaining task for anyone’s metaphysics is, (...) surprisingly, pragmatic. (shrink)

Despite its crucial role, sample representativeness remains a controversial topic in medical science methodology. There is an ongoing debate not only about how best to define and ensure the representativeness of a sample (e.g., Rudolph et al., 2023; Porta, 2016), but also about whether representativeness is worth pursuing at all (e.g., Rothman et al., 2013). We present a new definition of representativeness in terms of causal models and argue that it is more precise and more useful than existing alternatives. We (...) use examples to demonstrate the types of evidence that can support assumptions of representativeness. (shrink)

The theory and doctrine of proximate cause has been too easily dismissed. Two primary errors underlie this dismissal: a misunderstanding of “causal proximity,” and a mistaken inference from the correct observation that effects have multiple causes, to the claim that there is no hierarchy between proximate and more remote causes. This article defends the classic conception of proximate causation as causally grounded by reconstructing the doctrine and articulating an underlying concept of proximate causation in which proximity is relative (though still (...) objective). Proximate causation is a relation between two causes and an effect, when one cause mediates the effects of another. This idea informs the doctrines of proximate cause, which, following Bacon’s Maxim, trace back from the injury to its causes, in sequence. along the paths of causation, until responsibility is absorbed, at which point the process terminates. Causal chains “break”: not because the cause and effect are too remote, but because a set of causes that are proximate, relative to these remote causes, are sufficient to absorb responsibility. With responsibility absorbed, there is nothing further to trace back. This is missed if tracing is from cause to injury, rather than in reverse, from injury to cause. As a matter of responsibility, the responsibility of remote actors—those that act through the agency of others, or those that fail to prevent the wrongdoing of others—is always subordinate to that of the causally proximate actor. This does not mean that the proximate actor always bears greater responsibility than the remote one, but when responsibility is transmitted remotely, it must be in virtue of a justifying or excusing condition for the proximate actor’s act. The order of justification follows the order of causation. Properly understood, proximate cause is essentially a defense that asserts that the defendant is not the most proximate cause to the harm, and that someone (or something) else is. The implications for both liability and contribution are discussed. (shrink)

Some authors maintain that we can use causal Bayes nets to infer whether X → Y or X ← Y by consulting a probability distribution defined over some exogenous source of variation for X or Y . We raise a problem for this approach. Specifically, we point out that there are cases where an exogenous cause of X (Ex) has no probabilistic influence on Y no matter the direction of causation — namely, cases where Ex → X → Y and (...) Ex → X ← Y are probabilistically indistinguishable. We then assess the philosophical significance of this problem and discuss some potential solutions. (shrink)

While scholars of, and participants in, social movements, electoral politics, and organized labor are deeply engaged in contrasting different theories of how political actors should organize, little recent philosophical work has asked what social organizing is. This paper aims to answer this question in a way that can make sense of typical organizing‐related claims and debates. It is intuitive that what social organizing does is bring about some kind of collectivity. However, I argue that the varieties of collectivity most amply (...) theorized by analytic philosophers in recent years, including grouphood and collective intentionality, are not the right kinds to embed in a theory of social organizing. I ultimately argue that the sort of collectivity that organizing characteristically brings about is a special kind of causal complementarity among agents' actions— and that while this can exist alongside grouphood and collective intentionality, it is not the same thing as either. The notion of social organizing that emerges is one that can clarify, without trivializing, a number of pressing contemporary debates about how normal people should conduct themselves as interconnected political actors. (shrink)

In (Beckers, 2025) I introduced nondeterministic causal models as a generalization of Pearl’s standard deterministic causal models. I here take advantage of the increased expressivity offered by these models to offer a novel definition of actual causation (that also applies to deterministic models). Instead of motivating the definition by way of (often subjective) intuitions about examples, I proceed by developing it based entirely on the unique function that it can fulfil in communicating and learning a causal model. First I generalize (...) the more basic notion of counterfactual dependence, second I show how this notion has a vital role to play in the logic of causal discovery, third I introduce the notion of a structural simplification of a causal model, and lastly I bring both notions together in my definition of actual causation. Although novel, the resulting definition arrives at verdicts that are almost identical to those of my previous definition (Beckers, 2021, 2022). (shrink)

I generalize acyclic deterministic structural causal models to the nondeterministic case and argue that this offers an improved semantics for counterfactuals. The standard, deterministic, semantics developed by Halpern (and based on the initial proposal of Galles & Pearl) assumes that for each assignment of values to parent variables there is a unique assignment to their child variable, and it assumes that the actual world (an assignment of values to all variables of a model) specifies a unique counterfactual world for each (...) intervention. Both assumptions are unrealistic, and therefore I drop both of them in my proposal. I do so by allowing multi-valued functions in the structural equations. In addition, I adjust the semantics so that the solutions to the equations that obtained in the actual world are preserved in any counterfactual world. I provide a sound and complete axiomatization of the resulting logic and compare it to the standard one by Halpern and to more recent proposals that are closer to mine. Finally, I extend these models to the probabilistic case and show that they open up the way to identifying counterfactuals even in Causal Bayesian Networks. (shrink)

Many causal systems studied by sciences such as biology, pharmacology, and economics feature causal cycles. Most accounts of causal modelling currently on the market are, however, explicitly designed to study acyclic structures. This chapter focuses on causal cycles and the challenges such cycles pose for causal modelling. First, we distinguish between different types of causal cycles. Then we introduce causal models and discuss a selection of general challenges for cyclic models when it comes to representation, prediction, and causal discovery. Finally, (...) we zoom in on a concrete case from biochemistry: the PI3K/mTOR signalling network that plays a crucial role in regulating the cell cycle. Using this case we discuss some possible pitfalls for the application of causal modelling tools to complex biological cases. (shrink)

How feasible are conceptual engineering projects of social concepts that aim for the engineered concept to be deployed in people's ordinary conceptual practices? Predominant frameworks on the psychology of concepts that shape work on stereotyping, bias, and machine learning have grim implications for the prospects of conceptual engineers: conceptual engineering efforts are ineffective in promoting certain social‐conceptual changes. Since conceptual components that give rise to problematic social stereotypes are sensitive to statistical structures of the environment, purely conceptual change won't be (...) possible without corresponding world change. This tradition, however, tends to ignore that concepts don't only encode statistical, but also causal information. Paying attention to this feature of concepts, I argue, shows that conceptual engineering is not only possible. There is an imperative to conceptually‐engineer. (shrink)

A promising development in the philosophy of causation analyzes actual causation using structural equation models, i.e., “causal models”. This paper carefully considers what it means for an interpreted model to be accurate of its target situation. These considerations show, first, that our existing understanding of accuracy is inadequate. Further, and more controversially, they show that any causal model analysis is committed to a kind of relativism – a view whereby causation is a three-part relation holding between a cause, an effect, (...) and something else. In particular, insofar as a causal model analysis construes causation mind-and-language independently, it must treat causation as relative to a specification of background possibilities – i.e., a ‘modal profile.’ Or, so I argue. (shrink)