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  1. Causal Knowledge and the Process of Policy Making: Toward a Bottom-up Approach.Luis Mireles-Flores - 2024 - In Federica Russo & Phyllis Illari (eds.), The Routledge handbook of causality and causal methods. New York, NY: Routledge. pp. 571-587.
    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 (...)
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  2. Norms in Actual Causation.Jennifer McDonald - forthcoming - Erkenntnis.
    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 (...)
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  3. Eliciting the plurality of causal reasoning in social-ecological systems research.Tilman Hertz, T. Homas Banitz, Rodrigo Martínez-Peña, Sonja Radosavljevic, Emilie Lindkvist, Lars-Göran Johansson, Petri Ylikoski & Maja Schlüter - 2024 - Ecology and Society 29 (1).
    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 (...)
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  4. Bodies of evidence: The ‘Excited Delirium Syndrome’ and the epistemology of cause-of-death inquiry.Enno Fischer & Saana Jukola - 2024 - Studies in History and Philosophy of Science 104 (C):38-47.
    “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’ (...)
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  5. James Woodward: Causation with a Human Face: Normative Theory and Descriptive Psychology. [REVIEW]Enno Fischer - 2023 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 54 (4):625-629.
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  6. Rethinking Human and Machine Intelligence through Kant, Wittgenstein, Gödel, and Cantor.Jae Jeong Lee - manuscript
    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 (...)
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  7. Some Reflections on Causation.Yafeng Shan - 2024 - In Alternative Philosophical Approaches to Causation: Beyond Difference-making and Mechanism. Oxford: Oxford University Press. pp. 1-12.
    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 (...)
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  8. Review of Causation with a Human Face, James Woodward. [REVIEW]Holly Andersen - forthcoming - Philosophy of Science:1-6.
    I provide an overview and critical discussion of Causation with a Human Face, by James Woodward.
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  9. A Dialogue among Recent Views of Entity Realism.Mahdi Khalili - 2023 - Philosophy of Science:1-35.
    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 (...)
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  10. Reasoning Studies. From Single Norms to Individual Differences.Niels Skovgaard-Olsen - 2022 - Dissertation, University of Freiburg
    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.
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  11. Computing with causal theories.Erkan Tin & Varol Akman - 1992 - International Journal of Pattern Recognition and Artificial Intelligence 6 (4):699-730.
    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 (...)
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  12. The logic of counteraction.Erkan Tin & Varol Akman - 1993 - Elektrik 1 (3):167-181.
    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.
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  13. Risk, Precaution, and Causation.Masaki Ichinose - 2022 - Tetsugaku: International Journal of the Philosophical Association of Japan 6:22-53.
    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 (...)
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  14. Causality and attribution in an Aristotelian Theory.Srećko Kovač - 2015 - In Arnold Koslow & Arthur Buchsbaum (eds.), The Road to Universal Logic: Festschrift for 50th Birthday of Jean-Yves Béziauvol. 1, Cham, Heidelberg, etc.: Springer-Birkhäuser. Springer-Birkhäuser. pp. 327-340.
    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, (...)
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Causal Modeling
  1. (1 other version)Engineering social concepts: Feasibility and causal models.Eleonore Neufeld - 2024 - Philosophy and Phenomenological Research 109 (3):819-837.
    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 (...)
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  2. Causal Models and Causal Relativism.Jennifer McDonald - forthcoming - Synthese.
    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, (...)
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  3. The Logic of Counterfactuals and the Epistemology of Causal Inference.Hanti Lin - manuscript
    The 2021 Nobel Prize in Economics recognizes a type of causal model known as the Rubin causal model, or potential outcome framework, which deserves far more attention from philosophers than it currently receives. To spark philosophers' interest, I develop a dialectic connecting the Rubin causal model to the Lewis-Stalnaker debate on a logical principle of counterfactuals: Conditional Excluded Middle (CEM). I begin by playing good cop for CEM, developing a new argument in its favor---a Quine-Putnam-style indispensability argument. This argument is (...)
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  4. Just probabilities.Chad Lee-Stronach - 2024 - Noûs 58 (4):948-972.
    I defend the thesis that legal standards of proof are reducible to thresholds of probability. Many reject this thesis because it appears to permit finding defendants liable solely on the basis of statistical evidence. To the contrary, I argue – by combining Thomson's (1986) causal analysis of legal evidence with formal methods of causal inference – that legal standards of proof can be reduced to probabilities, but that deriving these probabilities involves more than just statistics.
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  5. A Causal Safety Criterion for Knowledge.Jonathan Vandenburgh - 2024 - Erkenntnis 89 (8):3287-3307.
    Safety purports to explain why cases of accidentally true belief are not knowledge, addressing Gettier cases and cases of belief based on statistical evidence. However, problems arise for using safety as a condition on knowledge: safety is not necessary for knowledge and cannot always explain the Gettier cases and cases of statistical evidence it is meant to address. In this paper, I argue for a new modal condition designed to capture the non-accidental relationship between facts and evidence required for knowledge: (...)
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  6. Causal modeling in multilevel settings: A new proposal.Thomas Blanchard & Andreas Hüttemann - 2024 - Philosophy and Phenomenological Research 109 (2):433-457.
    An important question for the causal modeling approach is how to integrate non‐causal dependence relations such as asymmetric supervenience into the approach. The most prominent proposal to that effect (due to Gebharter) is to treat those dependence relationships as formally analogous to causal relationships. We argue that this proposal neglects some crucial differences between causal and non‐causal dependencies, and that in the context of causal modeling non‐causal dependence relationships should be represented as mutual dependence relationships. We develop a new kind (...)
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  7. Actual Causation and the Challenge of Purpose.Enno Fischer - 2024 - Erkenntnis 89 (7):2925-2945.
    This paper explores the prospects of employing a functional approach in order to improve our concept of actual causation. Claims of actual causation play an important role for a variety of purposes. In particular, they are relevant for identifying suitable targets for intervention, and they are relevant for our practices of ascribing responsibility. I argue that this gives rise to the _challenge of purpose_. The challenge of purpose arises when different goals demand adjustments of the concept that pull in opposing (...)
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  8. (1 other version)A Causal Analysis of Harm.Sander Beckers, Hana Chockler & Joseph Y. Halpern - 2024 - Minds and Machines 34 (3):1-24.
    As autonomous systems rapidly become ubiquitous, there is a growing need for a legal and regulatory framework that addresses when and how such a system harms someone. There have been several attempts within the philosophy literature to define harm, but none of them has proven capable of dealing with the many examples that have been presented, leading some to suggest that the notion of harm should be abandoned and “replaced by more well-behaved notions”. As harm is generally something that is (...)
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  9. The Chances of Choices.Reuben Stern - forthcoming - British Journal for the Philosophy of Science.
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  10. Broken brakes and dreaming drivers: the heuristic value of causal models in the law.Enno Fischer - 2024 - European Journal for Philosophy of Science 14 (1):1-20.
    Recently, there has been an increased interest in employing model-based definitions of actual causation in legal inquiry. The formal precision of such approaches promises to be an improvement over more traditional approaches. Yet model-based approaches are viable only if suitable models of legal cases can be provided, and providing such models is sometimes difficult. I argue that causal-model-based definitions benefit legal inquiry in an indirect way. They make explicit the causal assumptions that need to be made plausible to defend a (...)
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  11. Causal counterfactuals without miracles or backtracking.J. Dmitri Gallow - 2022 - Philosophy and Phenomenological Research 107 (2):439-469.
    If the laws are deterministic, then standard theories of counterfactuals are forced to reject at least one of the following conditionals: 1) had you chosen differently, there would not have been a violation of the laws of nature; and 2) had you chosen differently, the initial conditions of the universe would not have been different. On the relevant readings—where we hold fixed factors causally independent of your choice—both of these conditionals appear true. And rejecting either one leads to trouble for (...)
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  12. Why adoption of causal modeling methods requires some metaphysics.Holly Andersen - 2024 - In Federica Russo & Phyllis Illari (eds.), The Routledge handbook of causality and causal methods. New York, NY: Routledge.
    I highlight a metaphysical concern that stands in the way of more widespread adoption of causal modeling techniques such as causal Bayes nets. Researchers in some fields may resist adoption due to concerns that they don't 'really' understand what they are saying about a system when they apply such techniques. Students in these fields are repeated exhorted to be cautious about application of statistical techniques to their data without a clear understanding of the conditions required for those techniques to yield (...)
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  13. Actual Causation: Apt Causal Models and Causal Relativism.Jennifer McDonald - 2022 - Dissertation, The Graduate Center, Cuny
    This dissertation begins by addressing the question of when a causal model is apt for deciding questions of actual causation with respect to some target situation. I first provide relevant background about causal models, explain what makes them promising as a tool for analyzing actual causation, and motivate the need for a theory of aptness as part of such an analysis (Chapter 1). I then define what it is for a model on a given interpretation to be accurate of, that (...)
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  14. Essential Structure for Causal Models.Jennifer McDonald - forthcoming - Australasian Journal of Philosophy.
    This paper introduces and defends a new principle for when a structural equation model is apt for analyzing actual causation. Any such analysis in terms of these models has two components: a recipe for reading claims of actual causation off an apt model, and an articulation of what makes a model apt. The primary focus in the literature has been on the first component. But the problem of structural isomorphs has made the second especially pressing (Hall 2007; Hitchcock 2007a). Those (...)
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  15. Anti-reductionist Interventionism.Reuben Stern & Benjamin Eva - 2023 - British Journal for the Philosophy of Science 74 (1):241-267.
    Kim’s causal exclusion argument purports to demonstrate that the non-reductive physicalist must treat mental properties (and macro-level properties in general) as causally inert. A number of authors have attempted to resist Kim’s conclusion by utilizing the conceptual resources of Woodward’s interventionist conception of causation. The viability of these responses has been challenged by Gebharter, who argues that the causal exclusion argument is vindicated by the theory of causal Bayesian networks (CBNs). Since the interventionist conception of causation relies crucially on CBNs (...)
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  16. How to Trace a Causal Process.J. Dmitri Gallow - 2022 - Philosophical Perspectives 36 (1):95-117.
    According to the theory developed here, we may trace out the processes emanating from a cause in such a way that any consequence lying along one of these processes counts as an effect of the cause. This theory gives intuitive verdicts in a diverse range of problem cases from the literature. Its claims about causation will never be retracted when we include additional variables in our model. And it validates some plausible principles about causation, including Sartorio's ‘Causes as Difference Makers’ (...)
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  17. Minimal Turing Test and Children's Education.Duan Zhang, Xiaoan Wu & Jijun He - 2022 - Journal of Human Cognition 6 (1):47-58.
    Considerable evidence proves that causal learning and causal understanding greatly enhance our ability to manipulate the physical world and are major factors that distinguish humans from other primates. How do we enable unintelligent robots to think causally, answer the questions raised with "why" and even understand the meaning of such questions? The solution is one of the keys to realizing artificial intelligence. Judea Pearl believes that to achieve human-like intelligence, researchers must start by imitating the intelligence of children, so he (...)
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  18. Causal Bayes nets and token-causation: Closing the gap between token-level and type-level.Alexander Gebharter & Andreas Hüttemann - 2025 - Erkenntnis 90 (1):43-65.
    Causal Bayes nets (CBNs) provide one of the most powerful tools for modelling coarse-grained type-level causal structure. As in other fields (e.g., thermodynamics) the question arises how such coarse-grained characterisations are related to the characterisation of their underlying structure (in this case: token-level causal relations). Answering this question meets what is called a “coherence-requirement” in the reduction debate: How are different accounts of one and the same system (or kind of system) related to each other. We argue that CBNs as (...)
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  19. Molinism: Explaining our Freedom Away.Nevin Climenhaga & Daniel Rubio - 2022 - Mind 131 (522):459-485.
    Molinists hold that there are contingently true counterfactuals about what agents would do if put in specific circumstances, that God knows these prior to creation, and that God uses this knowledge in choosing how to create. In this essay we critique Molinism, arguing that if these theses were true, agents would not be free. Consider Eve’s sinning upon being tempted by a serpent. We argue that if Molinism is true, then there is some set of facts that fully explains both (...)
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  20. (1 other version)Causal Modeling Semantics for Counterfactuals with Disjunctive Antecedents.Giuliano Rosella & Jan Sprenger - manuscript
    Causal Modeling Semantics (CMS, e.g., Galles and Pearl 1998; Pearl 2000; Halpern 2000) is a powerful framework for evaluating counterfactuals whose antecedent is a conjunction of atomic formulas. We extend CMS to an evaluation of the probability of counterfactuals with disjunctive antecedents, and more generally, to counterfactuals whose antecedent is an arbitrary Boolean combination of atomic formulas. Our main idea is to assign a probability to a counterfactual (A ∨ B) > C at a causal model M as a weighted (...)
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  21. Causation and the Problem of Disagreement.Enno Fischer - 2021 - Philosophy of Science 88 (5):773-783.
    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.
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  22. Robustness and Modularity.Trey Boone - forthcoming - British Journal for the Philosophy of Science.
    Functional robustness refers to a system’s ability to maintain a function in the face of perturbations to the causal structures that support performance of that function. Modularity, a crucial element of standard methods of causal inference and difference-making accounts of causation, refers to the independent manipulability of causal relationships within a system. Functional robustness appears to be at odds with modularity. If a function is maintained despite manipulation of some causal structure that supports that function, then the relationship between that (...)
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  23. Actual Causation.Enno Fischer - 2021 - Dissertation, Leibniz Universität Hannover
    In this dissertation I develop a pluralist theory of actual causation. I argue that we need to distinguish between total, path-changing, and contributing actual causation. The pluralist theory accounts for a set of example cases that have raised problems for extant unified theories and it is supported by considerations about the various functions of causal concepts. The dissertation also analyses the context-sensitivity of actual causation. I show that principled accounts of causal reasoning in legal inquiry face limitations and I argue (...)
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  24. (1 other version)An Interventionist’s Guide to Exotic Choice.Reuben Stern - 2021 - Mind 130 (518):537-566.
    In this paper, I use interventionist causal models to identify some novel Newcomb problems, and subsequently use these problems to refine existing interventionist treatments of causal decision theory. The new Newcomb problems that make trouble for existing interventionist treatments involve so-called ‘exotic choice’—that is, decision-making contexts where the agent has evidence about the outcome of her choice. I argue that when choice is exotic, the interventionist can adequately capture causal decision-theoretic reasoning by introducing a new interventionist approach to updating on (...)
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  25. Causal feature learning for utility-maximizing agents.David Kinney & David Watson - 2020 - In David Kinney & David Watson (eds.), International Conference on Probabilistic Graphical Models. pp. 257–268.
    Discovering high-level causal relations from low-level data is an important and challenging problem that comes up frequently in the natural and social sciences. In a series of papers, Chalupka etal. (2015, 2016a, 2016b, 2017) develop a procedure forcausal feature learning (CFL) in an effortto automate this task. We argue that CFL does not recommend coarsening in cases where pragmatic considerations rule in favor of it, and recommends coarsening in cases where pragmatic considerations rule against it. We propose a new technique, (...)
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  26. The causal structure of natural kinds.Olivier Lemeire - 2021 - Studies in History and Philosophy of Science Part A 85:200-207.
    One primary goal for metaphysical theories of natural kinds is to account for their epistemic fruitfulness. According to cluster theories of natural kinds, this epistemic fruitfulness is grounded in the regular and stable co- occurrence of a broad set of properties. In this paper, I defend the view that such a cluster theory is insufficient to adequately account for the epistemic fruitfulness of kinds. I argue that cluster theories can indeed account for the projectibility of natural kinds, but not for (...)
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  27. (2 other versions)Running up the flagpole to see if anyone salutes: A response to Woodward on causal and explanatory asymmetries.Katrina Elliott & Marc Lange - forthcoming - Theoria : An International Journal for Theory, History and Fundations of Science.
    Does smoke cause fire or does fire cause smoke? James Woodward’s “Flagpoles anyone? Causal and explanatory asymmetries” argues that various statistical independence relations not only help us to uncover the directions of causal and explanatory relations in our world, but also are the worldly basis of causal and explanatory directions. We raise questions about Woodward’s envisioned epistemology, but our primary focus is on his metaphysics. We argue that any alleged connection between statistical (in)dependence and causal/explanatory direction is contingent, at best. (...)
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  28. Homeostatic Property Cluster Theory without Homeostatic Mechanisms: Two Recent Attempts and their Costs.Yukinori Onishi & Davide Serpico - 2021 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie (1):61-82.
    The homeostatic property cluster theory is widely influential for its ability to account for many natural-kind terms in the life sciences. However, the notion of homeostatic mechanism has never been fully explicated. In 2009, Carl Craver interpreted the notion in the sense articulated in discussions on mechanistic explanation and pointed out that the HPC account equipped with such notion invites interest-relativity. In this paper, we analyze two recent refinements on HPC: one that avoids any reference to the causes of the (...)
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  29. Causal Inference from Noise.Nevin Climenhaga, Lane DesAutels & Grant Ramsey - 2021 - Noûs 55 (1):152-170.
    "Correlation is not causation" is one of the mantras of the sciences—a cautionary warning especially to fields like epidemiology and pharmacology where the seduction of compelling correlations naturally leads to causal hypotheses. The standard view from the epistemology of causation is that to tell whether one correlated variable is causing the other, one needs to intervene on the system—the best sort of intervention being a trial that is both randomized and controlled. In this paper, we argue that some purely correlational (...)
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  30. Correlation Isn’t Good Enough: Causal Explanation and Big Data. [REVIEW]Frank Cabrera - 2021 - Metascience 30 (2):335-338.
    A review of Gary Smith and Jay Cordes: The Phantom Pattern Problem: The Mirage of Big Data. New York: Oxford University Press, 2020.
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  31. The structure of epistemic probabilities.Nevin Climenhaga - 2020 - Philosophical Studies 177 (11):3213-3242.
    The epistemic probability of A given B is the degree to which B evidentially supports A, or makes A plausible. This paper is a first step in answering the question of what determines the values of epistemic probabilities. I break this question into two parts: the structural question and the substantive question. Just as an object’s weight is determined by its mass and gravitational acceleration, some probabilities are determined by other, more basic ones. The structural question asks what probabilities are (...)
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  32. Causal Models and the Logic of Counterfactuals.Jonathan Vandenburgh - manuscript
    Causal models show promise as a foundation for the semantics of counterfactual sentences. However, current approaches face limitations compared to the alternative similarity theory: they only apply to a limited subset of counterfactuals and the connection to counterfactual logic is not straightforward. This paper addresses these difficulties using exogenous interventions, where causal interventions change the values of exogenous variables rather than structural equations. This model accommodates judgments about backtracking counterfactuals, extends to logically complex counterfactuals, and validates familiar principles of counterfactual (...)
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  33. On the Concept and Conservation of Critical Natural Capital.C. Tyler DesRoches - 2020 - International Studies in the Philosophy of Science (N/A):1-22.
    Ecological economics is an interdisciplinary science that is primarily concerned with developing interventions to achieve sustainable ecological and economic systems. While ecological economists have, over the last few decades, made various empirical, theoretical, and conceptual advancements, there is one concept in particular that remains subject to confusion: critical natural capital. While critical natural capital denotes parts of the environment that are essential for the continued existence of our species, the meaning of terms commonly associated with this concept, such as ‘non-substitutable’ (...)
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  34. Horizontal Surgicality and Mechanistic Constitution.Michael Baumgartner, Lorenzo Casini & Beate Krickel - 2018 - Erkenntnis 85 (2):417-430.
    While ideal interventions are acknowledged by many as valuable tools for the analysis of causation, recent discussions have shown that, since there are no ideal interventions on upper-level phenomena that non-reductively supervene on their underlying mechanisms, interventions cannot—contrary to a popular opinion—ground an informative analysis of constitution. This has led some to abandon the project of analyzing constitution in interventionist terms. By contrast, this paper defines the notion of a horizontally surgical intervention, and argues that, when combined with some innocuous (...)
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  35. Hiddleston’s Causal Modeling Semantics and the Distinction between Forward-Tracking and Backtracking Counterfactuals.Kok Yong Lee - 2017 - Studies in Logic 10 (1):79-94.
    Some cases show that counterfactual conditionals (‘counterfactuals’ for short) are inherently ambiguous, equivocating between forward-tracking and backtracking counterfactu- als. Elsewhere, I have proposed a causal modeling semantics, which takes this phenomenon to be generated by two kinds of causal manipulations. (Lee 2015; Lee 2016) In an important paper (Hiddleston 2005), Eric Hiddleston offers a different causal modeling semantics, which he claims to be able to explain away the inherent ambiguity of counterfactuals. In this paper, I discuss these two semantic treatments (...)
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  36. Taking Control : The role of manipulation in theories of causation.Henning Strandin - 2019 - Dissertation, Stockholm University
    Causation has always been a philosophically controversial subject matter. While David Hume’s empiricist account of causation has been the dominant influence in analytic philosophy and science during modern times, a minority view has instead connected causation essentially to agency and manipulation. A related approach has for the first time gained widespread popularity in recent years, due to new powerful theories of causal inference in science that are based in a technical notion of intervention, and James Woodward’s closely connected interventionist theory (...)
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