I explore the problem of ``probabilistic causal preemption'' in the context of a``propensity trajectory'' theory of singular probabilistic causation. This involvesa particular conception of events and a substantive thesis concerning events soconceived.

This paper considers the applicability of standard accounts of causation to living systems. In particular it examines critically the increasing tendency to equate causal explanation with the identification of a mechanism. A range of differences between living systems and paradigm mechanisms are identified and discussed. While in principle it might be possible to accommodate an account of mechanism to these features, the attempt to do so risks reducing the idea of a mechanism to vacuity. It is proposed that the solution (...) to this problem requires the development of a philosophical account of process adequate to apply to living systems. (shrink)

This paper considers the applicability of standard accounts of causation to living systems. In particular it examines critically the increasing tendency to equate causal explanation with the identification of a mechanism. A range of differences between living systems and paradigm mechanisms are identified and discussed. While in principle it might be possible to accommodate an account of mechanism to these features, the attempt to do so risks reducing the idea of a mechanism to vacuity. It is proposed that the solution (...) to this problem requires the development of a philosophical account of process adequate to apply to living systems. (shrink)

In this paper I offer an 'integrating account' of singular causation, where the term 'integrating' refers to the following program for analysing causation. There are two intuitions about causation, both of which face serious counterexamples when used as the basis for an analysis of causation. The 'process' intuition, which says that causes and effects are linked by concrete processes, runs into trouble with cases of 'misconnections', where an event which serves to prevent another fails to do so on a particular (...) occasion and yet the two events are linked by causal processes. The chance raising intuition, according to which causes raise the chance of their effects, easily accounts for misconnections but faces the problem of chance lowering causes, a problem easily accounted for by the process approach. The integrating program attempts to provide an analysis of singular causation by synthesising the two insights, so as to solve both problems. In this paper I show that extant versions of the integrating program due to Eells, Lewis, and Menzies fail to account for the chance-lowering counterexample. I offer a new diagnosis of the chance lowering case, and use that as a basis for an integrating account of causation which does solve both cases. In doing so, I accept various assumptions of the integrating program, in particular that there are no other problems with these two approaches. As an example of the process account, I focus on the recent CQ theory of Wesley Salmon (1997). (shrink)

Causal pluralism is currently a hot topic in philosophy. However, the consequences of this view on causation for scientific knowledge and scientific methodology are heavily underexposed in the present debate. My aim in this paper is to argue that an epistemological-methodological point of view should be valued as a line of approach on its own and to demonstrate how epistemological- methodological causal pluralism differs in its scope from conceptual and metaphysical causal pluralism. Further, I defend epistemological-methodological causal pluralism and try (...) to illustrate that scientific practice needs diverse causal concepts in diverse domains, and even diverse causal concepts within singular domains. (shrink)

Which country, politician, or policy is more of a cause of the Covid-19 pandemic death toll? Which of the two factories causally contributed more to the pollution of the nearby river? A wide-ranging portion of our everyday thought and talk, and attitudes rely on a graded notion of causation. However, it is sometimes highlighted that on most contemporary accounts, causation is on-off. Some philosophers further question the legitimacy of talk of degrees of causation and suggest that we avoid it. Some (...) hold that the notion of degrees of causation is an illusion. In this paper, I’ll argue that causation does come in degrees. (shrink)

Critics of functional explanations in social science maintain that such explanations are illegitimate unless a mechanism is specified. Others argue that mechanisms are not necessary for causal inference and that functional explanations are a type of causal claim that raise no special difficulties for testing. I show that there is indeed a special problem that confronts testing functional explanations resulting from their connection to second-order causal claims. I explain how mechanisms can resolve this difficulty, but argue that this does not (...) provide support for methodological individualism since it is not necessary that the mechanisms be described in terms of individual interactions. (shrink)

What is exactly the emergence relation? In which sense is irreducibility associated with it besides being assumed by definition? Although in many cases the explanatory role of emergent states does not exceed the explanatory role of more basic states, this does not speak against the fact that, for some relevant explanatory contexts, emergent states are irreducible. On this basis, an epistemic concept of the emergence relation that does not depend strictly on irreducibility is here offered.

We propose an account of dynamic predicates which draws on the notion of force, eliminating reference to events in the linguistic semantics. We treat dynamic predicates as predicates of forces, represented as functions from an initial situation to a final situation that occurs ceteris paribus, that is, if nothing external intervenes. The possibility that opposing forces might intervene to prevent the transition to a given final situation leads us to a novel analysis of non-culminating accomplishment predicates in a variety of (...) languages, including the English progressive. We then apply the force-theoretic framework to the composition of basic Vendlerian eventuality types within a lexical-decomposition syntax. The difference between predicates of forces and predicates of situations is argued to underlie the dynamic/stative contrast, and also to allow for a formal treatment of the difference between be and stay. Consequences for the relationship between language and cognition are discussed. (shrink)

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 (...) good skeptical grounds for resisting any conclusions in favour of the automation of causal reasoning. If by causal reasoning is meant the entirety of the process through which we discover causal relationships and make use of this knowledge in prediction, explanation, decision-making, and reasoning in terms of counterfactuals, then one relies besides on tacit knowledge, as might be constituted by or derived from the epistemic faculty virtues and abilities of the causal reasoner, the value systems and character traits of the causal reasoner, the implicit knowledge base available to the causal reasoner, and the habits that sustain our causal reasoning practices. While certain aspects of causal reasoning may be axiomatized and formalized and algorithms may be implemented to approximate causal reasoning, one has to remain skeptical about whether causal reasoning may be fully automated. This demonstration will involve an engagement with Meno’s Paradox. (shrink)

The foundations of probability are viewed through the lens of the subjectivist interpretation. This article surveys conditional probability, arguments for probabilism, probability dynamics, and the evidential and subjective interpretations of probability.

Christopher Hitchcocks discussion of my use of screening-off in analyzing the causal process of natural selection raises some interesting issues to which I am pleased to reply. The bulk of his article is devoted to some fairly general points in the theory of explanation. In particular, he questions whether or not my point that phenotype screens off genotype from reproductive success (in cases of organismic selection) supports my claim that the explanation of differential reproductive success should be in terms of (...) phenotypic differences, not genotypic differences. I will respond to this and show why the two supposed counter-examples to my position fail. (shrink)

One goal of recent philosophy of mind has been to ‘naturalize’ intentionality by showing how a purely physical system could have states that represent or are about items in the world. The project is reductionist in spirit, the aim being to explain intentional relations—to say what they really are—and to do so in terms that do not themselves utilize intentional or semantic concepts. In this vein there are attempts to explain intentional relations in terms of causal relations, informational relations, teleological (...) or functional relations, relations involving abstract similarity or isomorphism, and various combinations thereof. What makes these accounts naturalistic is the presumed objectivity and scientific respectability of the properties appelated to in the explanans. What makes them all reductive is their shared presumption that intentionality can be explained in terms that have a wider application to intentional systems as well as to systems that have no mental properties at all. (shrink)

I am very grateful to Stephan Leuenberger, Erica Shumener and Naomi Thompson for their excellent and thoughtful commentaries on Making Things Up. I have learned a lot from thinking through their replies. As it happens, they focus on pretty disparate aspects of the book: necessitation, relative fundamentality, and what builds the building facts, respectively. I will thus engage with their remarks separately.

For a long time, regularity accounts of causation have virtually vanished from the scene. Problems encountered within other theoretical frameworks have recently induced authors working on causation, laws of nature, or methodologies of causal reasoning – as e.g. May (Kausales Schliessen. Eine Untersuchung über kausale Erklärungen und Theorienbildung. Ph.D. thesis, Universität Hamburg, Hamburg, 1999), Ragin (Fuzzy-set social science. Chicago: University of Chicago Press, 2000), Graßhoff and May (Causal regularities. In W. Spohn, M. Ledwig, & M. Esfeld (Eds.), Current issues in (...) causation (pp. 85–114). Paderborn: Mentis, 2001), Swartz (The concept of physical law (2nd ed.). http://www.sfu.ca/philosophy/physical-law/, 2003), Halpin (Erkenntnis, 58, 137–168, 2003) – to direct their attention back to regularity theoretic analyses. In light of the latest proposals of regularity theories, the paper at hand therefore reassesses the criticism raised against regularity accounts since the INUS theory of causation of Mackie (The cement of the universe. A study of causation. Oxford: Clarendon Press, 1974). It is shown that most of these objections target strikingly over-simplified regularity theoretic sketches. By outlining ways to refute these objections it is argued that the prevalent conviction as to the overall failure of regularity theories has been hasty. (shrink)

The essential precondition of implementing interventionist techniques of causal reasoning is that particular variables are identified as so-called intervention variables. While the pertinent literature standardly brackets the question how this can be accomplished in concrete contexts of causal discovery, the first part of this paper shows that the interventionist nature of variables cannot, in principle, be established based only on an interventionist notion of causation. The second part then demonstrates that standard observational methods that draw on Bayesian networks identify intervention (...) variables only if they also answer the questions that can be answered by interventionist techniques—which are thus rendered dispensable. The paper concludes by suggesting a way of identifying intervention variables that allows for exploiting the whole inferential potential of interventionist techniques. (shrink)

Manipulationism holds that information about the results of interventions is of utmost importance for scientific practices such as causal assessment or explanation. Specifically, manipulation provides information about the stability, or invariance, of the relationship between X and Y: were we to wiggle the cause X, the effect Y would accordingly wiggle and, additionally, the relation between the two will not be disrupted. This sort of relationship between variables are called 'invariant empirical generalisations'. The paper focuses on questions about causal assessment (...) and analyses the status of manipulation. It is argued that manipulationism is trapped in a dilemma. If manipulationism is read as providing a conceptual analysis of causation, then it fails to provide a story about the methods for causal assessment. If, instead, manipulationism is read as providing a method for causal assessment, then it is at an impasse concerning causal assessment in areas where manipulations are not performed. Empirical generalisations are then reassessed, in such a way that manipulation is not taken as methodologically fundamental. The paper concludes that manipulation is the appropriate tool for some scientific contexts, but not for all. (shrink)

This paper deals with causal analysis in the social sciences. We first present a conceptual framework according to which causal analysis is based on a rationale of variation and invariance, and not only on regularity. We then develop a formal framework for causal analysis by means of structural modelling. Within this framework we approach causality in terms of exogeneity in a structural conditional model based which is based on (i) congruence with background knowledge, (ii) invariance under a large variety of (...) environmental changes, and (iii) model fit. We also tackle the issue of confounding and show how latent confounders can play havoc with exogeneity. This framework avoids making untestable metaphysical claims about causal relations and yet remains useful for cognitive and action-oriented goals. (shrink)

In her ‘Causality and Determination’, Anscombe argues for the strong thesis that despite centuries of philosophical assumption to the contrary, the supposition that causality and necessity have something essential to do with one another is baseless. In this paper, I assess Anscombe’s arguments and endorse her conclusion. I then attempt to argue that her arguments remain highly relevant today, despite the fact that most popular general views of causation today are firmly probabilistic in orientation and thus show no trace of (...) the assumptions Anscombe hoped to undermine. My suggestion is that Anscombe's interests in causality are distinct from those which mostly animate the modern debate about the general nature of causality and that in those specialized areas of philosophy in which those concerns still dominate, one can still see the effects of the fallacies and confusions to which she alerts us. I conclude by offering two possible complementary explanations of the tendency to suppose that causation is a variety of necessitation. (shrink)

This paper addresses questions about how the levels of causality (generic and single-case causality) are related. One question is epistemological: can relationships at one level be evidence for relationships at the other level? We present three kinds of answer to this question, categorised according to whether inference is top-down, bottom-up, or the levels are independent. A second question is metaphysical: can relationships at one level be reduced to relationships at the other level? We present three kinds of answer to this (...) second question, categorised according to whether single-case relations are reduced to generic, generic relations are reduced to single-case, or the levels are independent. We then explore causal inference in autopsy. This is an interesting case study, we argue, because it refutes all three epistemologies and all three metaphysics. We close by sketching an account of causality that survives autopsy—the epistemic theory. (shrink)

In a recent article in this journal, Federica Russo and Jon Williamson argue that an analysis of causality in terms of probabilistic relationships does not do justice to the use of mechanistic evidence to support causal claims. I will present Ronald Giere's theory of probabilistic causation, and show that it can account for the use of mechanistic evidence (both in the health sciences—on which Russo and Williamson focus—and elsewhere). I also review some other probabilistic theories of causation (of Suppes, Eells, (...) and Humphreys) and show that they cannot account for the use of mechanistic evidence. I argue that these theories are also inferior to Giere's theory in other respects. (shrink)

The goal of research in social epidemiology is not simply conceptual clarification or theoretical understanding, but more importantly it is to contribute to, and enhance the health of populations (and so, too, the people who constitute those populations). Undoubtedly, understanding how various individual risk factors such as smoking and obesity affect the health of people does contribute to this goal. However, what is distinctive of much on-going work in social epidemiology is the view that analyses making use of individual-level variables (...) is not enough. In the spirit of Durkheim and Weber, S. Leonard Syme makes this point by writing that just “as bad water and food may be harmful to our health, unhealthful forces in our society may be detrimental to our capacity to make choices and to form opinions” conducive to health and well-being. Advocates of upstream (distal) causes of adverse health outcomes propose to identify the most important of these “unhealthful forces” as the fundamental causes of adverse health outcomes. However, without a clear, theoretically precise and well-grounded understanding of the characteristics of fundamental causes, there is little hope in applying the statistical tools of the health sciences to hypotheses about fundamental causes, their outcomes, and policies intended to enhance the health of populations. This paper begins the process of characterizing the social epidemiological concept of fundamental cause in a theoretically respectable and robust way. (shrink)

Nancy Cartwright begins her recent book, Hunting Causes and Using Them, by noting that while a few years ago real causal claims were in dispute, nowadays “causality is back, and with a vengeance.” In the case of the social sciences, Keith Morrison writes that “Social science asks ‘why?’. Detecting causality or its corollary—prediction—is the jewel in the crown of social science research.” With respect to the health sciences, Judea Pearl writes that the “research questions that motivate most studies in the (...) health sciences are causal in nature.” However, not all data used by people interested in making causal claims come from experiments that use random assignment to control and treatment groups. Indeed, much research in the social and health science depends on non-experimental, observational data. Thus, one of the most important problems in the social and health sciences concerns making warranted causal claims using non-experimental, observational data; viz., “Can observational data be used to make etiological inferences leading to warranted causal claims?” This paper examines one method of warranting causal claims that is especially widespread in epidemiology and the health sciences generally—the use of causal criteria. It is argued that cases of complex causation generally, and redundant causation—both causal overdetermination and causal preemption—specifically, undermine the use of such criteria to warrant causal claims. (shrink)

I argue that information is a goal-relative concept for Bayesians. More precisely, I argue that how much information is provided by a piece of evidence depends on whether the goal is to learn the truth or to rank actions by their expected utility, and that different confirmation measures should therefore be used in different contexts. I then show how information measures may reasonably be derived from confirmation measures, and I show how to derive goal-relative non-informative and informative priors given background (...) information. Finally, I argue that my arguments have important implications for both objective and subjective Bayesianism. In particular, the Uniqueness Thesis is either false or must be modified. Moreover, objective Bayesians must concede that pragmatic factors systematically influence which priors are rational, and subjective Bayesians must concede that pragmatic factors sometimes partly determine which prior distribution most accurately represents an agent's epistemic state. (shrink)

It is widely thought that there is an important argument to be made that starts with premises taken from the science of physics and ends with the conclusion of physicalism. The standard view is that this argument takes the form of a causal argument for physicalism. Roughly, physics tells us that the physical realm is causally complete, and so minds (among other entities) must be physical if they are to interact with the world as we think they do. In what (...) follows, I raise problems for this view. After an initial review of the causal argument, I begin my case by showing that the totality of physical truths do not deductively entail the causal completeness of the physical realm, using a double-prevention scenario and causation by omission to show that nonphysical causes of physical effects would not need to violate physical conservation laws. I then move on to raise problems for an inductive argument for causal completeness by drawing on the neo-Russellian view that there is no causation in fundamental physics, and so causation must itself be a realized or derived entity. I conclude by suggesting that the underlying problem is that the causal argument has fallen out of touch with the sophisticated understanding that philosophers have developed of the role of causation within physics. (shrink)

Why do people get sick? I argue that a disease explanation is best thought of as causal network instantiation, where a causal network describes the interrelations among multiple factors, and instantiation consists of observational or hypothetical assignment of factors to the patient whose disease is being explained. This paper first discusses inference from correlation to causation, integrating recent psychological discussions of causal reasoning with epidemiological approaches to understanding disease causation, particularly concerning ulcers and lung cancer. It then shows how causal (...) mechanisms represented by causal networks can contribute to reasoning involving correlation and causation. The understanding of causation and causal mechanisms provides the basis for a presentation of the causal network instantiation model of medical explanation. (shrink)

In this paper two accounts of Reichenbachian common cause systems are compared. Examples are provided which show that Hofer-Szabó and Rédei account and Hofer-Szabó et al. account is compatible, with but not equivalent, to Mazzola’s. Moreover, the difference of the two accounts with respect to their explanatory adequacy is discussed, in the light of Salmon’s statistical-relevance approach to statistical explanation.

Though common sense says that causes must temporally precede their effects, the hugely influential interventionist account of causation makes no reference to temporal precedence. Does common sense lead us astray? In this paper, I evaluate the power of the commonsense assumption from within the interventionist approach to causal modeling. I first argue that if causes temporally precede their effects, then one need not consider the outcomes of interventions in order to infer causal relevance, and that one can instead use temporal (...) and probabilistic information to infer exactly when X is causally relevant to Y in each of the senses captured by Woodward’s interventionist treatment. Then, I consider the upshot of these findings for causal decision theory, and argue that the commonsense assumption is especially powerful when an agent seeks to determine whether so-called “dominance reasoning” is applicable. (shrink)

Philosophers discussing causation take on, as one of their responsibilities, the task of specifying an ontology of causation. Both standard and non-standard accounts of that ontology make two assumptions: that the ontological category of causal relata admits a unique specification, and that cause and effect are of the same ontological type. These assumptions are rarely made explicit, but there is in fact little reason to think them true. It is argued here that, if the question has any interest, there are (...) some considerations in favor of rejecting Uniqueness and Uniformity, no good reasons in favor of them, but good reason to think that we are not in a position to make a decisive pronouncement, since some of the information needed to make a judgement on the matter is empirical in character. Philosophers have therefore made two mistakes about the relata of causation: one is to have accepted unwarranted restrictions on the categories pertinent to causation; the other is to have wrongly assumed that we are in a position to name them. (shrink)

Debates in animal cognition are frequently polarized between the romantic view that some species have human-like causal understanding and the killjoy view that human causal reasoning is unique. These apparently endless debates are often characterized by conceptual confusions and accusations of straw-men positions. What is needed is an account of causal understanding that enables researchers to investigate both similarities and differences in cognitive abilities in an incremental evolutionary framework. Here we outline the ways in which a three-dimensional model of causal (...) understanding fulfills these criteria. We describe how this approach clarifies what is at stake, illuminates recent experiments on both physical and social cognition, and plots a path for productive future research that avoids the romantic/killjoy dichotomy. (shrink)

Data from medical research are typically summarized with various types of outcome measures. We present three arguments in favor of absolute over relative outcome measures. The first argument is from cognitive bias: relative measures promote the reference class fallacy and the overestimation of treatment effectiveness. The second argument is decision-theoretic: absolute measures are superior to relative measures for making a decision between interventions. The third argument is causal: interpreted as measures of causal strength, absolute measures satisfy a set of desirable (...) properties, but relative measures do not. Absolute outcome measures outperform relative measures on all counts. (shrink)

This paper develops axiomatic foundations for a probabilistic-interventionist theory of causal strength. Transferring methods from Bayesian confirmation theory, I proceed in three steps: I develop a framework for defining and comparing measures of causal strength; I argue that no single measure can satisfy all natural constraints; I prove two representation theorems for popular measures of causal strength: Pearl's causal effect measure and Eells' difference measure. In other words, I demonstrate these two measures can be derived from a set of plausible (...) adequacy conditions. The paper concludes by sketching future research avenues. (shrink)

Causal reasoning is one of our most central cognitive competencies, enabling us to adapt to our world. Causal knowledge allows us to predict future events, or diagnose the causes of observed facts. We plan actions and solve problems using knowledge about cause-effect relations. Without our ability to discover and empirically test causal theories, we would not have made progress in various empirical sciences. In the past decades, the important role of causal knowledge has been discovered in many areas of cognitive (...) psychology. Despite the ubiquity of causal reasoning, textbooks of cognitive psychology have neglected this growing field. The goal of The Oxford Handbook of Causal Reasoning is to fill this gap. The handbook brings together the leading researchers in the field of causal reasoning and offers state-of-the-art presentations of theories and research. It provides introductions of competing theories of causal reasoning, and discusses its role in various cognitive functions and domains. The final section presents research from neighboring fields. 1. Causal Reasoning: An Introduction Michael R. Waldmann Part I: Theories of Causal Cognition 2. Associative Accounts of Causal Cognition Mike E. Le Pelley, Oren Griffiths, and Tom Beesley 3. Rules of Causal Judgment: Mapping Statistical Information Onto Causal Beliefs José C. Perales, Andrés Catena, Antonio Cándido, and Antonio Maldonado 4. The Inferential Reasoning Theory of Causal Learning: Toward a Multi- Process Propositional Account Yannick Boddez, Jan De Houwer, and Tom Beckers 5. Causal Invariance as an Essential Constraint for Creating a Causal Representation of the World: Generalizing the Invariance of Causal Power Patricia W. Cheng and Hongjing Lu 6. The Acquisition and Use of Causal Structure Knowledge Benjamin Margolin Rottman 7. Formalizing Prior Knowledge in Causal Induction Thomas L. Griffiths 8. Causal Mechanisms Samuel G. B. Johnson and Woo-kyoung Ahn 9. Force Dynamics and Causation Phillip Wolff and Robert Thorstad 10. Mental Models and Causation P. N. Johnson- Laird and Sangeet S. Khemlani 11. Pseudocontingencies Klaus Fiedler and Florian Kutzner 12. Singular Causation David Danks 13. Cognitive Neuroscience of Causal Reasoning Joachim T. Operskalski and Aron K. Barbey Part II: Basic Cognitive Functions 14. Visual Impressions of Causality Peter White 15. Goal-Directed Actions Bernhard Hommel 16. Planning and Control Magda Osman 17. Reinforcement Learning and Causal Models Samuel J. Gershman 18. Causation and the Probability of Causal Conditionals David E. Over 19. Causal Models and Conditional Reasoning Mike Oaksford and Nick Chater 20. Concepts as Causal Models: Categorization Bob Rehder 21. Concepts as Causal Models: Induction Bob Rehder 22. Causal Explanation Tania Lombrozo and Nadya Vasilyeva 23. Diagnostic Reasoning Björn Meder and Ralf Mayrhofer 24. Inferring Causal Relations by Analogy Keith J. Holyoak and Hee-Seung Lee 25. Causal Argument Ulrike Hahn, Roland Bluhm, and Frank Zenker 26. Causality in Decision- Making York Hagmayer and Philip M. Fernbach Part III: Domains of Causal Reasoning 27. Intuitive Theories Tobias Gerstenberg and Joshua B. Tenenbaum 28. Space, Time, and Causality Marc J. Buehner 29. Causation in Legal and Moral Reasoning David A. Lagnado and Tobias Gerstenberg 30. The Role of Causal Knowledge in Reasoning About Mental Disorders Woo-kyoung Ahn, Nancy S. Kim, and Matthew S. Lebowitz 31. Causality and Causal Reasoning in Natural Language Torgrim Solstad and Oliver Bott 32. Social Attribution and Explanation Denis Hilton Part IV: Development, Phylogeny, and Culture 33. The Development of Causal Reasoning Paul Muentener and Elizabeth Bonawitz 34. Causal Reasoning in Non-Human Animals Christian Schloegl and Julia Fischer 35. Causal Cognition and Culture Andrea Bender, Sieghard Beller, and Douglas L. Medin. (shrink)

To clarify and illuminate the place of probability in science Ellery Eells and James H. Fetzer have brought together some of the most distinguished philosophers ...

The anti-causal prophecies of last century have been disproved. Causality is neither a ‘relic of a bygone’ nor ‘another fetish of modern science’; it still occupies a large part of the current debate in philosophy and the sciences. This investigation into causal modelling presents the rationale of causality, i.e. the notion that guides causal reasoning in causal modelling. It is argued that causal models are regimented by a rationale of variation, nor of regularity neither invariance, thus breaking down the dominant (...) Human paradigm. The notion of variation is shown to be embedded in the scheme of reasoning behind various causal models: e.g. Rubin’s model, contingency tables, and multilevel analysis. It is also shown to be latent – yet fundamental – in many philosophical accounts. Moreover, it has significant consequences for methodological issues: the warranty of the causal interpretation of causal models, the levels of causation, the characterisation of mechanisms, and the interpretation of probability. This book offers a novel philosophical and methodological approach to causal reasoning in causal modelling and provides the reader with the tools to be up to date about various issues causality rises in social science. (shrink)

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 (...) with realist sympathies have reason to resist the standard response to this problem, which introduces a normative parameter into the metaphysics (Hall 2007; Halpern and Hitchcock 2010, 2015; Halpern 2016a; Menzies 2017; Gallow 2021). However, the only alternative solution in the literature leaves central questions unanswered (Blanchard and Schaffer 2017). I propose an independently motivated aptness requirement, Evident Mediation, that provides the missing details and resolves the structural isomorph problem without need for a normative parameter. (shrink)

I argue for responsibility internalism. That is, moral responsibility (i.e., accountability, or being apt for praise or blame) depends only on factors internal to agents. Employing this view, I also argue that no one is responsible for what AI does but this isn’t morally problematic in a way that counts against developing or using AI. Responsibility is grounded in three potential conditions: the control (or freedom) condition, the epistemic (or awareness) condition, and the causal responsibility condition (or consequences). I argue (...) that causal responsibility is irrelevant for moral responsibility, and that the control condition and the epistemic condition depend only on factors internal to agents. Moreover, since what AI does is at best a consequence of our actions, and the consequences of our actions are irrelevant to our responsibility, no one is responsible for what AI does. That is, the so-called responsibility gap exits. However, this isn’t morally worrisome for developing or using AI. Firstly, I argue, current AI doesn’t generate a new kind of concern about responsibility that the older technologies don’t. Then, I argue that responsibility gap is not worrisome because neither responsibility gap, nor my argument for its existence, entails that no one can be justly punished, held accountable, or incurs duties in reparations when AI causes a harm. (shrink)

Laws of nature take center stage in philosophy of science. Laws are usually believed to stand in a tight conceptual relation to many important key concepts such as causation, explanation, confirmation, determinism, counterfactuals etc. Traditionally, philosophers of science have focused on physical laws, which were taken to be at least true, universal statements that support counterfactual claims. But, although this claim about laws might be true with respect to physics, laws in the special sciences (such as biology, psychology, economics etc.) (...) appear to have—maybe not surprisingly—different features than the laws of physics. Special science laws—for instance, the economic law “Under the condition of perfect competition, an increase of demand of a commodity leads to an increase of price, given that the quantity of the supplied commodity remains constant” and, in biology, Mendel's Laws—are usually taken to “have exceptions”, to be “non-universal” or “to be ceteris paribus laws”. How and whether the laws of physics and the laws of the special sciences differ is one of the crucial questions motivating the debate on ceteris paribus laws. Another major, controversial question concerns the determination of the precise meaning of “ceteris paribus”. Philosophers have attempted to explicate the meaning of ceteris paribus clauses in different ways. The question of meaning is connected to the problem of empirical content, i.e., the question whether ceteris paribus laws have non-trivial and empirically testable content. Since many philosophers have argued that ceteris paribus laws lack empirically testable content, this problem constitutes a major challenge to a theory of ceteris paribus laws. (shrink)

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 (...) of causation in philosophy. This monograph assesses five manipulationist or interventionist theories of causation, viewed as theories that purport to tell us what causation is by providing us with the meaning of causal claims. It is shown that they cannot do this, as the conditions on causation that they impose are too weak, mainly due to ineliminable circularities in their definitions of causal terms. It is then argued that a subset of Woodward’s theory can nevertheless contribute crucially to an explanation of the unique role that manipulation has in our acquisition of causal knowledge. This explanation differs from the common regularist explanation of the epistemic utility of manipulation and experiment, and it is taken to confirm several important manipulationist intuitions. However, the success of the explanation depends on (this subset of) interventionism not itself being understood as a theory of causation, but as a theory of intervention. (shrink)

Questions about the metaphysics of causation may be usefully divided as follows. First, there are questions about the nature of the causal relata, including (1.1) whether they are in spacetime immanence), (1.2) how fine grained they are individuation), and (1.3) how many there are adicity). Second, there are questions about the metaphysics of the causal relation, including (2.1) what is the difference between causally related and causally unrelated sequences connection), (2.2) what is the difference between sequences related as cause to (...) effect, and those related as effect to cause or as joint effects of a common cause direction), and (2.3) what is the difference between sequences involving the cause, and those involving mere conditions selection). (shrink)

“Probabilistic Causation” designates a group of theories that aim to characterize the relationship between cause and effect using the tools of probability theory. The central idea behind these theories is that causes change the probabilities of their effects. This article traces developments in probabilistic causation, including recent developments in causal modeling. A variety of issues within, and objections to, probabilistic theories of causation will also be discussed.