Causal Modeling

Edited by Alexander Gebharter (Ludwig Maximilians Universität, München)
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 2000
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402 found
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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. Causal Counterfactuals Without Miracles or Backtracking.J. Dmitri Gallow - manuscript
    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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  3. How to Trace a Causal Process.J. Dmitri Gallow - manuscript
    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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  4. 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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  5. 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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  6. 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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  7. Causal Models and the Logic of Counterfactuals.Jonathan Vandenburgh - manuscript
    Causal models provide a framework for making counterfactual predictions, making them useful for evaluating the truth conditions of counterfactual sentences. However, current causal models for counterfactual semantics face limitations compared to the alternative similarity-based approach: they only apply to a limited subset of counterfactuals and the connection to counterfactual logic is not straightforward. This paper argues that these limitations arise from the theory of interventions where intervening on variables requires changing structural equations rather than the values of variables. Using an (...)
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  8. A Causal Safety Criterion for Knowledge.Jonathan Vandenburgh - manuscript
    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, numerous examples suggest that safety fails as a condition on knowledge: a belief can be safe even when one's evidence is clearly insufficient for knowledge and knowledge is compatible with the nearby possibility of error, a situation ruled out by the safety condition. In this paper, I argue for a new modal condition designed to capture (...)
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  9. Causal Modeling and the Efficacy of Action.Holly Andersen - forthcoming - In Michael Brent (ed.), 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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  10. 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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  11. Causal Reasoning and Meno’s Paradox.Melvin Chen & Lock Yue Chew - forthcoming - AI and Society:1-9.
    Causal reasoning is an aspect of learning, reasoning, and decision-making that involves the cognitive ability to discover relationships between causal relata, learn and understand these causal relationships, and make use of this causal knowledge in prediction, explanation, decision-making, and reasoning in terms of counterfactuals. Can we fully automate causal reasoning? One might feel inclined, on the basis of certain groundbreaking advances in causal epistemology, to reply in the affirmative. The aim of this paper is to demonstrate that one still has (...)
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  12. Molinism: Explaining Our Freedom Away.Nevin Climenhaga & Daniel Rubio - forthcoming - Mind:fzab042.
    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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  13. 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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  14. 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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  15. Free Will, Control, and the Possibility to Do Otherwise From a Causal Modeler’s Perspective.Alexander Gebharter, Maria Sekatskaya & Gerhard Schurz - forthcoming - Erkenntnis:1-18.
    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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  16. Bayesian Networks and Causal Ecumenism.David Kinney - forthcoming - Erkenntnis:1-26.
    Proponents of various causal exclusion arguments claim that for any given event, there is often a unique level of granularity at which that event is caused. Against these causal exclusion arguments, causal ecumenists argue that the same event or phenomenon can be caused at multiple levels of granularity. This paper argues that the Bayesian network approach to representing the causal structure of target systems is consistent with causal ecumenism. Given the ubiquity of Bayesian networks as a tool for representing causal (...)
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  17. Antireductionist Interventionism.Reuben Stern & Benjamin Eva - forthcoming - British Journal for the Philosophy of Science.
    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 (2005) interventionist conception of causation. The viability of these responses has been challenged by Gebharter (2017a), 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 (...)
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  18. Interventionism and Over-Time Causal Analysis in Social Sciences.Tung-Ying Wu - forthcoming - Philosophy of the Social Sciences.
    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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  19. 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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  20. 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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  21. 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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  22. Realism Without Interphenomena: Reichenbach’s Cube, Sober’s Evidential Realism, and Quantum.Florian J. Boge - 2021 - International Studies in the Philosophy of Science 33 (4):231-246.
    In ‘Reichenbach's cubical universe and the problem of the external world’, Elliott Sober attempts a refutation of solipsism à la Reichenbach. I here contrast Sober's line of argument with observati...
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  23. 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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  24. 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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  25. 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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  26. 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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  27. 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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  28. Quantifying Proportionality and the Limits of Higher-Level Causation and Explanation.Alexander Gebharter & Markus Ilkka Eronen - 2021 - British Journal for the Philosophy of Science.
    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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  29. 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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  30. 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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  31. 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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  32. 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 (N/A):1-22.
    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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  33. 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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  34. The Counteridentical Account of Explanatory Identities.Isaac Wilhelm - 2021 - Journal of Philosophy 118 (2):57-78.
    Many explanations rely on identity facts. In this paper, I propose an account of how identity facts explain: roughly, the fact that A is identical to B explains another fact whenever that other fact depends, counterfactually, on A being identical to B. As I show, this account has many virtues. It avoids several problems facing accounts of explanatory identities, and when precisified using structural equations, it can be used to defend interventionist accounts of causation against an objection.
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  35. Structural Decision Theory.Tung-Ying Wu - 2021 - Philosophy of Science 88 (5):951-960.
    Judging an act’s causal efficacy plays a crucial role in causal decision theory. A recent development appeals to the causal modeling framework with an emphasis on the analysis of intervention based on the causal Bayes net for clarifying what causally depends on our acts. However, few writers have focused on exploring the usefulness of extending structural causal models to decision problems that are not ideal for intervention analysis. The thesis concludes that structural models provide a more general framework for rational (...)
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  36. The Compatibility of Differential Equations and Causal Models Reconsidered.Wes Anderson - 2020 - Erkenntnis 85 (2):317-332.
    Weber argues that causal modelers face a dilemma when they attempt to model systems in which the underlying mechanism operates according to some set of differential equations. The first horn is that causal models of these systems leave out certain causal effects. The second horn is that causal models of these systems leave out time-dependent derivatives, and doing so distorts reality. Either way causal models of these systems leave something important out. I argue that Weber’s reasons for thinking causal modeling (...)
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  37. A Tale of Two Deficits: Causality and Care in Medical AI.Melvin Chen - 2020 - Philosophy and Technology 33 (2):245-267.
    In this paper, two central questions will be addressed: ought we to implement medical AI technology in the medical domain? If yes, how ought we to implement this technology? I will critically engage with three options that exist with respect to these central questions: the Neo-Luddite option, the Assistive option, and the Substitutive option. I will first address key objections on behalf of the Neo-Luddite option: the Objection from Bias, the Objection from Artificial Autonomy, the Objection from Status Quo, and (...)
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  38. 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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  39. 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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  40. Causal Discovery and the Problem of Psychological Interventions.Markus I. Eronen - 2020 - New Ideas in Psychology 59:100785.
    Finding causes is a central goal in psychological research. In this paper, I argue based on the interventionist approach to causal discovery that the search for psychological causes faces great obstacles. Psychological interventions are likely to be fat-handed: they change several variables simultaneously, and it is not known to what extent such interventions give leverage for causal inference. Moreover, due to problems of measurement, the degree to which an intervention was fat-handed, or more generally, what the intervention in fact did, (...)
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  41. Interventionism and Mental Surgery.Alex Kaiserman - 2020 - Erkenntnis 85 (4):919-935.
    John Campbell has claimed that the interventionist account of causation must be amended if it is to be applied to causation in psychology. The problem, he argues, is that it follows from the so-called ‘surgical’ constraint that intervening on psychological states requires the suspension of the agent’s rational autonomy. In this paper, I argue that the problem Campbell identifies is in fact an instance of a wider problem for interventionism, extending beyond psychology, which I call the problem of ‘abrupt transitions’. (...)
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  42. Causal Feature Learning for Utility-Maximizing Agents.David Kinney & David Watson - 2020 - In 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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  43. Predicting Motor Imagery Performance From Resting-State EEG Using Dynamic Causal Modeling.Minji Lee, Jae-Geun Yoon & Seong-Whan Lee - 2020 - Frontiers in Human Neuroscience 14.
  44. Causal Structures in Language and Thought.Eleonore Neufeld - 2020 - Dissertation, University of Southern California
    This dissertation defends the view that concepts encode causal information and, for the first time, applies this view to a range of topics in the philosophy of language and social philosophy. In my first chapter (“Cognitive Essentialism and the Structure of Concepts”), I survey the current empirical and theoretical literature on causal-essentialist theories of concepts. In my second chapter (“Meaning Externalism and Causal Model Theory”), I propose an account of natural kind concepts according to which they encode statistical information of (...)
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  45. A New Proposal How to Handle Counterexamples to Markov Causation À la Cartwright, Or: Fixing the Chemical Factory.Nina Retzlaff & Alexander Gebharter - 2020 - Synthese 197 (4):1467-1486.
    Cartwright (Synthese 121(1/2):3–27, 1999a; The dappled world, Cambridge University Press, Cambridge, 1999b) attacked the view that causal relations conform to the Markov condition by providing a counterexample in which a common cause does not screen off its effects: the prominent chemical factory. In this paper we suggest a new way to handle counterexamples to Markov causation such as the chemical factory. We argue that Cartwright’s as well as similar scenarios feature a certain kind of non-causal dependence that kicks in once (...)
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  46. The Impact of Culture on Corruption, Gross Domestic Product, and Human Development.Wolfgang Scholl & Carsten C. Schermuly - 2020 - Journal of Business Ethics 162 (1):171-189.
    The evidence of culture’s impact on corruption and its consequences is still inconclusive despite several investigations: Sometimes, theory is lacking and causes and consequences seem exchangeable. Based on psychological research on the distribution and use of power, we predicted that a steeper distribution of power induces more corruption and elaborated its negative consequences in a complex causal model. For measuring power distribution, pervading national culture, we augmented Hofstede’s ‘Power Distance’ with three additional indicators into a reversed, more reliable and valid (...)
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  47. Normative commitments, causal structure, and policy disagreement.Georgie Statham - 2020 - Synthese 197 (5):1983-2003.
    Recently, there has been a large amount of support for the idea that causal claims can be sensitive to normative considerations. Previous work has focused on the concept of actual causation, defending the claim that whether or not some token event c is a cause of another token event e is influenced by both statistical and prescriptive norms. I focus on the policy debate surrounding alternative energies, and use the causal modelling framework to show that in this context, people’s normative (...)
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  48. Cyclic and Multilevel Causation in Evolutionary Processes.Jonathan Warrell & Mark Gerstein - 2020 - Biology and Philosophy 35 (5):1-36.
    Many models of evolution are implicitly causal processes. Features such as causal feedback between evolutionary variables and evolutionary processes acting at multiple levels, though, mean that conventional causal models miss important phenomena. We develop here a general theoretical framework for analyzing evolutionary processes drawing on recent approaches to causal modeling developed in the machine-learning literature, which have extended Pearls do-calculus to incorporate cyclic causal interactions and multilevel causation. We also develop information-theoretic notions necessary to analyze causal information dynamics in our (...)
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  49. Causal Complexity, Conditional Independence, and Downward Causation.James Woodward - 2020 - Philosophy of Science 87 (5):857-867.
    This article defends the notion of downward causation, relating it to a notion of conditional independence.
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  50. Explanatory Conditionals.Holger Andreas - 2019 - Philosophy of Science 86 (5):993–1004.
    The present paper aims to complement causal model approaches to causal explanation by Woodward [15], Halpern and Pearl [5], and Strevens [14]. It centres on a strengthened Ramsey Test of conditionals: α ≫ γ iff, after sus- pending judgment about α and γ, an agent can infer γ from the supposition of α. It has been shown by Andreas and Gu ̈nther [1] that such a conditional can be used as starting point of an analysis of ‘because’ in natural language. (...)
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