About this topic
Summary Causal modeling consists in the study, development, and application of causal models. A causal model is a formal device intended to represent a part of the causal structure of the world. It comprises several variables and specifies how (and if) these variables are causally connected to each other. Causal models are used in many disciplines (such as statistics, computer science, philosophy, econometrics, and epidemiology) to study cause-effect relationships, to formulate complex causal hypotheses, and to predict the effects of possible interventions. 
Introductions Pearl 2000; Spirtes et al 1993
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  1. Should causal models always be Markovian? The case of multi-causal forks in medicine.Donald Gillies & Aidan Sudbury - 2013 - European Journal for Philosophy of Science 3 (3):275-308.
    The development of causal modelling since the 1950s has been accompanied by a number of controversies, the most striking of which concerns the Markov condition. Reichenbach's conjunctive forks did satisfy the Markov condition, while Salmon's interactive forks did not. Subsequently some experts in the field have argued that adequate causal models should always satisfy the Markov condition, while others have claimed that non-Markovian causal models are needed in some cases. This paper argues for the second position by considering the multi-causal (...)
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  2. How to Analyse Retrodictive Probabilities in Inference to the Best Explanation.Andrew Holster - manuscript
    IBE ('Inference to the best explanation' or abduction) is a popular and highly plausible theory of how we should judge the evidence for claims of past events based on present evidence. It has been notably developed and supported recently by Meyer following Lipton. I believe this theory is essentially correct. This paper supports IBE from a probability perspective, and argues that the retrodictive probabilities involved in such inferences should be analysed in terms of predictive probabilities and a priori probability ratios (...)
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  3. Reducing the Dauer Larva: molecular models of biological phenomena in Caenorhabditis elegans research.Arciszewski Michal - manuscript
    One important aspect of biological explanation is detailed causal modeling of particular phenomena in limited experimental background conditions. Recognising this allows a new avenue for intertheoretic reduction to be seen. Reductions in biology are possible, when one fully recognises that a sufficient condition for a reduction in biology is a molecular model of 1) only the demonstrated causal parameters of a biological model and 2) only within a replicable experimental background. These intertheoretic identifications –which are ubiquitous in biology and form (...)
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  4. 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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  5. A reply to Rose, Livengood, Sytsma, and Machery.Chandra Sripada, Richard Gonzalez, Daniel Kessler, Eric Laber, Sara Konrath & Vijay Nair - manuscript
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  6. 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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  7. Causal modeling in multilevel settings: A new proposal.Thomas Blanchard & Andreas Hüttemann - forthcoming - Philosophy and Phenomenological Research.
    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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  8. 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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  9. 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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  10. Three Concepts of Actual Causation.Enno Fischer - forthcoming - British Journal for the Philosophy of Science.
    I argue that we need to distinguish between three concepts of actual causation: total, path-changing, and contributing actual causation. I provide two lines of argument in support of this account. First, I address three thought experiments that have been troublesome for unified accounts of actual causation, and I show that my account provides a better explanation of corresponding causal intuitions. Second, I provide a functional argument: if we assume that a key purpose of causal concepts is to guide agency, we (...)
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  11. Actual Causation and the Challenge of Purpose.Enno Fischer - forthcoming - Erkenntnis:1-21.
    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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  12. Causal Bayes nets and token-causation: Closing the gap between token-level and type-level.Alexander Gebharter & Andreas Hüttemann - forthcoming - Erkenntnis:1-23.
    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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  13. Just Probabilities.Chad Lee-Stronach - forthcoming - Noûs.
    I defend the thesis that legal standards of proof are reducible to thresholds of probability. Many have rejected this thesis because it seems to entail that defendants can be found liable solely on the basis of statistical evidence. I argue that this inference is invalid. I do so by developing a view, called Legal Causalism, that combines Thomson's (1986) causal analysis of evidence with recent work in formal theories of causal inference. On this view, legal standards of proof can be (...)
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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. Engineering Social Concepts: Feasibility and Causal Models.Eleonore Neufeld - forthcoming - Philosophy and Phenomenological Research.
    How feasible are conceptual engineering projects of social concepts that aim for the engineered concept to be widely adopted in ordinary everyday life? 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. Specifically, since conceptual components that give rise to problematic social stereotypes are sensitive to statistical structures of the environment, purely conceptual change won’t (...)
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  16. Causal modeling semantics for counterfactuals with disjunctive antecedents.Giuliano Rosella & Jan Sprenger - forthcoming - Annals of Pure and Applied Logic.
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  17. The Chances of Choices.Reuben Stern - forthcoming - British Journal for the Philosophy of Science.
    It is sometimes thought that if we treat decision-theoretic options as interventions, then we can use evidential decision theory to vindicate causal dominance reasoning. This is supposed to be guaranteed by a causal modeling axiom that implies that interventions are probabilistically independent of their non-effects---namely, the Causal Markov Condition. But there are two concerns for this line of reasoning. First, the Causal Markov Condition doesn’t imply that an agent should regard their intervention as probabilistically independent from its non-effects when the (...)
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  18. A Causal Safety Criterion for Knowledge.Jonathan Vandenburgh - forthcoming - Erkenntnis:1-21.
    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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  19. 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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  20. Why adoption of causal modeling methods requires some metaphysics.Holly Andersen - 2023 - In Federica Russo (ed.), Routledge Handbook of Causality and Causal Methods,. 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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  21. An Inferential Theory of Causal Reasoning.Alexander Bochman - 2023 - In Natasha Alechina, Andreas Herzig & Fei Liang (eds.), Logic, Rationality, and Interaction: 9th International Workshop, LORI 2023, Jinan, China, October 26–29, 2023, Proceedings. Springer Nature Switzerland. pp. 1-16.
    We present a general formalism of causal reasoning that encompasses both Pearl’s approach to causality and a number of key systems of nonmonotonic reasoning in artificial intelligence.
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  22. Well-Defined Interventions and Causal Variable Choice.Zili Dong - 2023 - Philosophy of Science 90 (2):395-412.
    There has been much debate among scientists and philosophers about what it means for interventions invoked in causal inference to be “well-defined” and how considerations of this sort should constrain the choice of causal variables. In this paper, I propose that an intervention is well-defined just in case the effect of interest is well-defined, and that the intervention can serve as a suitable means to identify that effect. Based on this proposal, I identify several types of ambiguous intervention. Implications for (...)
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  23. Causal Variable Choice, Interventions, and Pragmatism.Zili Dong - 2023 - Dissertation, University of Western Ontario
    The past century has witnessed numerous methodological innovations in probabilistic and statistical methods of causal inference (e.g., the graphical modelling and the potential outcomes frameworks, as introduced in Chapter 1). These innovations have not only enhanced the methodologies by which scientists across diverse domains make causal inference, but they have also made a profound impact on the way philosophers think about causation. The philosophical issues discussed in this thesis are stimulated and inspired by these methodological innovations. Chapter 2 addresses the (...)
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  24. Quantifying proportionality and the limits of higher-level causation and explanation.Alexander Gebharter & Markus Ilkka Eronen - 2023 - British Journal for the Philosophy of Science 74 (3):573-601.
    Supporters of the autonomy of higher-level causation (or explanation) often appeal to proportionality, arguing that higher-level causes are more proportional than their lower-level realizers. Recently, measures based on information theory and causal modeling have been proposed that allow one to shed new light on proportionality and the related notion of specificity. In this paper we apply ideas from this literature to the issue of higher vs. lower-level causation (and explanation). Surprisingly, proportionality turns out to be irrelevant for the question of (...)
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  25. Unification and explanation from a causal perspective.Alexander Gebharter & Christian J. Feldbacher-Escamilla - 2023 - Studies in History and Philosophy of Science Part A 99 (C):28-36.
    We discuss two influential views of unification: mutual information unification (MIU) and common origin unification (COU). We propose a simple probabilistic measure for COU and compare it with Myrvold’s (2003, 2017) probabilistic measure for MIU. We then explore how well these two measures perform in simple causal settings. After highlighting several deficiencies, we propose causal constraints for both measures. A comparison with explanatory power shows that the causal version of COU is one step ahead in simple causal settings. However, slightly (...)
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  26. 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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  27. Conjoined cases.Tomasz Wysocki - 2023 - Synthese 201 (6):1-19.
    Incorporating normality ascriptions into counterfactual theories of causation was supposed to handle isomorphs. It doesn’t—conjoining isomorphs can produce cases that such ascriptions cannot resolve.
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  28. Causal bias in measures of inequality of opportunity.Lennart B. Ackermans - 2022 - Synthese 200 (6):1-31.
    In recent decades, economists have developed methods for measuring the country-wide level of inequality of opportunity. The most popular method, called the ex-ante method, uses data on the distribution of outcomes stratified by groups of individuals with the same circumstances, in order to estimate the part of outcome inequality that is due to these circumstances. I argue that these methods are potentially biased, both upwards and downwards, and that the unknown size of this bias could be large. To argue that (...)
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  29. Causal Modeling and the Efficacy of Action.Holly Andersen - 2022 - In Michael Brent & Lisa Miracchi Titus (eds.), Mental Action and the Conscious Mind. Routledge.
    This paper brings together Thompson's naive action explanation with interventionist modeling of causal structure to show how they work together to produce causal models that go beyond current modeling capabilities, when applied to specifically selected systems. By deploying well-justified assumptions about rationalization, we can strengthen existing causal modeling techniques' inferential power in cases where we take ourselves to be modeling causal systems that also involve actions. The internal connection between means and end exhibited in naive action explanation has a modal (...)
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  30. 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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  31. 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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  32. 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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  33. A Causal Bayes Net Analysis of Glennan’s Mechanistic Account of Higher-Level Causation.Alexander Gebharter - 2022 - British Journal for the Philosophy of Science 73 (1):185-210.
    One of Stuart Glennan's most prominent contributions to the new mechanist debate consists in his reductive analysis of higher-level causation in terms of mechanisms (Glennan, 1996). In this paper I employ the causal Bayes net framework to reconstruct his analysis. This allows for specifying general assumptions which have to be satis ed to get Glennan's approach working. I show that once these assumptions are in place, they imply (against the background of the causal Bayes net machinery) that higher-level causation indeed (...)
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  34. Free Will, Control, and the Possibility to do Otherwise from a Causal Modeler’s Perspective.Alexander Gebharter, Maria Sekatskaya & Gerhard Schurz - 2022 - Erkenntnis 87 (4):1889-1906.
    Strong notions of free will are closely connected to the possibility to do otherwise as well as to an agent’s ability to causally influence her environment via her decisions controlling her actions. In this paper we employ techniques from the causal modeling literature to investigate whether a notion of free will subscribing to one or both of these requirements is compatible with naturalistic views of the world such as non-reductive physicalism to the background of determinism and indeterminism. We argue that (...)
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  35. On the causal interpretation of heritability from a structural causal modeling perspective.Qiaoying Lu & Pierrick Bourrat - 2022 - Studies in History and Philosophy of Science Part A 94 (C):87-98.
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  36. 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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  37. Enough blanket metaphysics, time for data-driven heuristics.Wiktor Rorot, Tomasz Korbak, Piotr Litwin & Marcin Miłkowski - 2022 - Behavioral and Brain Sciences 45:e206.
    Bruineberg and colleagues criticisms' have been received but downplayed in the free energy principle (FEP) literature. We strengthen their points, arguing that Friston blanket discovery, even if tractable, requires a full formal description of the system of interest at the outset. Hence, blanket metaphysics is futile, and we postulate that researchers should turn back to heuristic uses of Pearl blankets.
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  38. Interventionism and Over-Time Causal Analysis in Social Sciences.Tung-Ying Wu - 2022 - Philosophy of the Social Sciences 52 (1-2):3-24.
    The interventionist theory of causation has been advertised as an empirically informed and more nuanced approach to causality than the competing theories. However, previous literature has not yet analyzed the regression discontinuity (hereafter, RD) and the difference-in-differences (hereafter, DD) within an interventionist framework. In this paper, I point out several drawbacks of using the interventionist methodology for justifying the DD and RD designs. However, I argue that the first step towards enhancing our understanding of the DD and RD designs from (...)
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  39. 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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  40. A New Halpern-Pearl Definition of Actual Causality by Appealing to the Default World.Fan Zhu - 2022 - Axiomathes 32 (2):453-472.
    Halpern and Hitchcock appealed to the normality of witness worlds to solve the problem of isomorphism in the Halpern-Pearl definition of actual causality. This paper first proposes a new isomorphism counterexample, called “bogus permission,” to show that their approach is unsuccessful. Then, to solve the problem of isomorphism, I propose a new improvement over the Halpern-Pearl definition by introducing default worlds. Finally, I demonstrate that my new definition can resolve more potential counterexamples than similar approaches in the current literature, including (...)
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  41. Causal Sufficiency and Actual Causation.Sander Beckers - 2021 - Journal of Philosophical Logic 50 (6):1341-1374.
    Pearl opened the door to formally defining actual causation using causal models. His approach rests on two strategies: first, capturing the widespread intuition that X = x causes Y = y iff X = x is a Necessary Element of a Sufficient Set for Y = y, and second, showing that his definition gives intuitive answers on a wide set of problem cases. This inspired dozens of variations of his definition of actual causation, the most prominent of which are due (...)
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  42. Correction to: Causal Sufficiency and Actual Causation.Sander Beckers - 2021 - Journal of Philosophical Logic 50 (6):1375-1375.
    A Correction to this paper has been published: https://doi.org/10.1007/s10992-021-09632-6.
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  43. A logical theory of causality.Alexander Bochman - 2021 - Cambridge, Massachusetts: MIT Press.
    "The first book that provides a systematic and rigorous logical theory of causality"--.
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  44. 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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  45. 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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  46. 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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  47. 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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  48. A Model-Invariant Theory of Causation.J. Dmitri Gallow - 2021 - Philosophical Review 130 (1):45-96.
    I provide a theory of causation within the causal modeling framework. In contrast to most of its predecessors, this theory is model-invariant in the following sense: if the theory says that C caused (didn't cause) E in a causal model, M, then it will continue to say that C caused (didn't cause) E once we've removed an inessential variable from M. I suggest that, if this theory is true, then we should understand a cause as something which transmits deviant or (...)
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  49. A causal Bayes net analysis of dispositions.Alexander Gebharter & Florian Fischer - 2021 - Synthese 198 (5):4873-4895.
    In this paper we develop an analysis of dispositions by means of causal Bayes nets. In particular, we analyze dispositions as cause-effect structures that increase the probability of the manifestation when the stimulus is brought about by intervention in certain circumstances. We then highlight several advantages of our analysis and how it can handle problems arising for classical analyses of dispositions such as masks, mimickers, and finks.
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  50. Combining causal Bayes nets and cellular automata: A hybrid modelling approach to mechanisms.Alexander Gebharter & Daniel Koch - 2021 - British Journal for the Philosophy of Science 72 (3):839-864.
    Causal Bayes nets (CBNs) can be used to model causal relationships up to whole mechanisms. Though modelling mechanisms with CBNs comes with many advantages, CBNs might fail to adequately represent some biological mechanisms because—as Kaiser (2016) pointed out—they have problems with capturing relevant spatial and structural information. In this paper we propose a hybrid approach for modelling mechanisms that combines CBNs and cellular automata. Our approach can incorporate spatial and structural information while, at the same time, it comes with all (...)
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