Causal claims are context sensitive. According to the old orthodoxy (Mackie 1974, Lewis 1986, inter alia), the context sensitivity of causal claims is all due to conversational pragmatics. According to the new contextualists (Hitchcock 1996, Woodward 2003, Maslen 2004, Menzies 2004, Schaffer 2005, and Hall ms), at least some of the context sensitivity of causal claims is semantic in nature. I want to discuss the prospects for causal contextualism, by asking why causal claims are context sensitive, what they are sensitive (...) to, and where they are sensitive to it. (shrink)

Humans often attribute the things that happen to one or another actual cause. In this chapter, we survey some recent philosophical and psychological research on causal attribution. We pay special attention to the relation between graphical causal modeling and theories of causal attribution. We think that the study of causal attribution is one place where formal and experimental techniques nicely complement one another.

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)

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 (...) structure and function fails to be manipulable independent of other causal relationships within the system. Contrary to this line of reasoning, I argue that functional robustness often attends feedback control, rather than failures of modularity. Feedback control poses its own challenges to causal explanation and inference, but those challenges do not undermine modularity—and indeed, modularity is crucial to grappling with them. (shrink)

How do people understand questions about cause and prevent? Some theories propose that people affirm that A causes B if A's occurrence makes a difference to B's occurrence in one way or another. Other theories propose that A causes B if some quantity or symbol gets passed in some way from A to B. The aim of our studies is to compare these theories' ability to explain judgements of causation and prevention. We describe six experiments that compare judgements for causal (...) paths that involve a mechanism, i.e. a continuous process of transmission or exchange from cause to effect, against paths that involve no mechanism yet a change in the cause nevertheless brings about a change in the effect. Our results show that people prefer to attribute cause when a mechanism links cause to effect. In contrast, prevention is sensitive both to the presence of an interruption to a causal mechanism and to a change in the outcome in the absence of a mechanism. In this sense, ‘prevent’ means something different than ‘cause not'. We discuss the implications of our results for existing theories of causation. (shrink)

It is often argued that higher-level special-science properties cannot be causally efficacious since the lower-level physical properties on which they supervene are doing all the causal work. This claim is usually derived from an exclusion principle stating that if a higher-level property F supervenes on a physical property F* that is causally sufficient for a property G, then F cannot cause G. We employ an account of causation as difference-making to show that the truth or falsity of this principle is (...) a contingent matter and derive necessary and sufficient conditions under which a version of it holds. We argue that one important instance of the principle, far from undermining non-reductive physicalism, actually supports the causal autonomy of certain higher-level properties. (shrink)

The paper investigates the kind of dependence relation that best portrays Machian frame-dragging in general relativity. The question is tricky because frame-dragging relates local inertial frames to distant distributions of matter in a time-independent way, thus establishing some sort of non-local link between the two. For this reason, a plain causal interpretation of frame-dragging faces huge challenges. The paper will shed light on the issue by using a generalized structural equation model analysis in terms of manipulationist counterfactuals recently applied in (...) the context of metaphysical enquiry by Schaffer (2016) and Wilson (2017). The verdict of the analysis will be that frame-dragging is best understood in terms of a novel type of dependence relation that is half-way between causation and grounding. (shrink)

We describe an approach to measuring biological information where ‘information’ is understood in the sense found in Francis Crick’s foundational contributions to molecular biology. Genes contain information in this sense, but so do epigenetic factors, as many biologists have recognized. The term ‘epigenetic’ is ambiguous, and we introduce a distinction between epigenetic and exogenetic inheritance to clarify one aspect of this ambiguity. These three heredity systems play complementary roles in supplying information for development. -/- We then consider the evolutionary significance (...) of the three inheritance systems. Whilst the genetic inheritance system was the key innovation in the evolution of heredity, in modern organisms the three systems each play important and complementary roles in heredity and evolution. -/- Our focus in the earlier part of the paper is on ‘proximate biology’, where information is a substantial causal factor that causes organisms to develop and causes offspring to resemble their parents. But much philosophical work has focused on information in ‘ultimate biology’. Ultimate information is a way of talking about the evolutionary design of the mechanisms of development and inheritance. We conclude by clarifying the relationship between the two. Ultimate information is not a causal factor that acts in development or heredity, but it can help to explain the evolution of proximate information, which is. (shrink)

Non-reductive interventionist theories of causation and methodologies of causal reasoning embedded in that theoretical framework have become increasingly popular in recent years. This paper argues that one variant of an interventionist account of causation, viz. the one presented, for example, in Woodward (2003), is unsuited as a theoretical fundament of interventionist methodologies of causal reasoning, because it renders corresponding methodologies incapable of uncovering a causal structure in a finite number of steps. This finding runs counter to Woodward's own assessment and (...) to other recent studies which presume that Woodward's version of interventionism is effectively applicable to uncover causal structures, e.g. Campbell (2007) or Shapiro and Sober (2007). (shrink)

Robert Merton's essays on theories of the middle range and his essays on functional explanation and the structural approach are among the most influential in the history of sociology. But their import is a puzzle. He explicitly allied himself with some of the most extreme scientistic formalists and contributed to and endorsed the Columbia model of theory construction. But Merton never responded to criticisms by Ernest Nagel of his arguments or acknowledged the rivalry between Lazarsfeld and Herbert Simon, rarely cited (...) the philosophical and methodological literature, and responded to critics with ambiguous concessions, leaving the Mertonian legacy profoundly ambiguous. Key Words: Robert Merton • Paul Lazarsfeld • theory construction • middle range theory • causal modeling • Émile Durkheim. (shrink)

The question of whether cultural transmission is faithful has attracted significant debate over the last 30 years. The degree of fidelity with which an object is transmitted depends on 1) the features chosen to be relevant, and 2) the quantity of details given about those features. Once these choices have been made, an object is described at a particular grain. In the absence of conventions between different researchers and across different fields about which grain to use, transmission fidelity cannot be (...) evaluated because it is relative to the choice of grain. In biology, because a genotype-to-phenotype mapping exists and transmission occurs from genotype to genotype, a privileged grain of description exists that circumvents this ‘grain problem.’ In contrast, in cultural evolution, the genotype–phenotype distinction cannot be drawn, rendering claims about fidelity dependent upon researchers’ choices. Thus, due to a lack of unified conventions, claims about fidelity transmission are difficult to evaluate. (shrink)

I extend work from Krakauer et al. (2020), who propose a conception of individuality as the capacity to propagate information through time. From this conception, they develop information-theoretic measures. I identify several shortcomings with these measures—in particular, that they are associative rather than causal. I rectify this shortcoming by deriving a causal information-theoretic measure of individuality. I then illustrate how this measure can be implemented and extended in the context of evolutionary transitions in individuality.

The ability to learn the direction of causal relations is critical for understanding and acting in the world. We investigated how children learn causal directionality in situations in which the states of variables are temporally dependent (i.e., autocorrelated). In Experiment 1, children learned about causal direction by comparing the states of one variable before versus after an intervention on another variable. In Experiment 2, children reliably inferred causal directionality merely from observing how two variables change over time; they interpreted Y (...) changing without a change in X as evidence that Y does not influence X. Both of these strategies make sense if one believes the variables to be temporally dependent. We discuss the implications of these results for interpreting previous findings. More broadly, given that many real-world environments are characterized by temporal dependency, these results suggest strategies that children may use to learn the causal structure of their environments. (shrink)

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 (...) the Lewisian causal dependence, Menziesian causal dependence, and modified version of the Halpern-Pearl definition. Some other advantages of my definition are also discussed. (shrink)

Mathematical modeling is a convenient way for characterization of complex ecosystems. This approach was applied to study the dynamics of zooplankton in Lake Sevan (Armenia) at different stages of anthropogenic eutrophication with the use of a novel method called discrete modeling of dynamical systems with feedback (DMDS). Simulation demonstrated that the application of this method helps in characterization of inter- and intra-component relationships in a natural ecosystem. This method describes all possible pairwise inter-component relationships like “plus–plus,” “minus–minus,” “plus–minus,” “plus–zero,” “minus–zero,” (...) and “zero–zero” that occur in most ecosystems. Based on the results, a working hypothesis was formulated. It was found that the sensitivity to weak external influence in zooplanktons was the greatest during the mid period of eutrophication in Lake Sevan, whereas in the final stages of eutrophication, an outbreak in the biomass production of cyanobacteria was evident. To support this approach, a weak external disturbance in the form of magnetic storm was used to see its effect on species Daphnia longispina sevanica. A statistically significant correlation between the frequency of magnetic storms and the number of this species was revealed and an increase in the number of toxic cyanobacteria species as a consequence of eutrophication. This paper, for the first time, suggests a DMDS method, to diagnose impact of anthropogenic eutrophication on environment. (shrink)

In the causal inference framework of Spirtes, Glymour, and Scheines, inferences about causal relationships are made from samples from probability distributions and a number of assumptions relating causal relations to probability distributions. The most controversial of these assumptions is the Causal Faithfulness Assumption, which roughly states that if a conditional independence statement is true of a probability distribution generated by a causal structure, it is entailed by the causal structure and not just for particular parameter values. In this paper we (...) show that the addition of the Causal Faithfulness Assumption plays three quite different roles in the SGS framework: it reduces the degree of underdetermination of causal structure by probability distribution; computationally, it justifies reliable causal inference algorithms that would otherwise have to be slower in order to be reliable; and statistically, it implies that those algorithms reliably obtain the correct answer at smaller sample sizes than would otherwise be the case. We also consider a number of variations on the Causal Faithfulness Assumption, and show how they affect each of these three roles. (shrink)

This paper has two main parts. In the first part, we motivate a kind of indeterminate, suppositional credences by discussing the prospect for a subjective interpretation of a causal Bayesian network, an important tool for causal reasoning in artificial intelligence. A CBN consists of a causal graph and a collection of interventional probabilities. The subjective interpretation in question would take the causal graph in a CBN to represent the causal structure that is believed by an agent, and interventional probabilities in (...) a CBN to represent suppositional credences. We review a difficulty noted in the literature with such an interpretation, and suggest that a natural way to address the challenge is to go for a generalization of CBN that allows indeterminate credences. In the second part, we develop a decision-theoretic foundation for such indeterminate suppositional credences, by generalizing a theory of coherent choice functions to accommodate some form of act-state dependence. The upshot is a decision-theoretic framework that is not only rich enough to, so to speak, ground the probabilities in a subjectively interpreted causal network, but also interesting in its own right, in that it accommodates both act-state dependence and imprecise probabilities. (shrink)

Much of the recent work on the epistemology of causation has centered on two assumptions, known as the Causal Markov Condition and the Causal Faithfulness Condition. Philosophical discussions of the latter condition have exhibited situations in which it is likely to fail. This paper studies the Causal Faithfulness Condition as a conjunction of weaker conditions. We show that some of the weaker conjuncts can be empirically tested, and hence do not have to be assumed a priori. Our results lead to (...) two methodologically significant observations: (1) some common types of counterexamples to the Faithfulness condition constitute objections only to the empirically testable part of the condition; and (2) some common defenses of the Faithfulness condition do not provide justification or evidence for the testable parts of the condition. It is thus worthwhile to study the possibility of reliable causal inference under weaker Faithfulness conditions. As it turns out, the modification needed to make standard procedures work under a weaker version of the Faithfulness condition also has the practical effect of making them more robust when the standard Faithfulness condition actually holds. This, we argue, is related to the possibility of controlling error probabilities with finite sample size (“uniform consistency”) in causal inference. (shrink)

In the artificial intelligence literature a promising approach to counterfactual reasoning is to interpret counterfactual conditionals based on causal models. Different logics of such causal counterfactuals have been developed with respect to different classes of causal models. In this paper I characterize the class of causal models that are Lewisian in the sense that they validate the principles in Lewis’s well-known logic of counterfactuals. I then develop a system sound and complete with respect to this class. The resulting logic is (...) the weakest logic of causal counterfactuals that respects Lewis’s principles, sits in between the logic developed by Galles and Pearl and the logic developed by Halpern, and stands to Galles and Pearl’s logic in the same fashion as Lewis’s stands to Stalnaker’s. (shrink)

As many philosophers agree, the frame problem is concerned with how an agent may efficiently filter out irrelevant information in the process of problem-solving. Hence, how to solve this problem hinges on how to properly handle semantic relevance in cognitive modeling, which is an area of cognitive science that deals with simulating human's cognitive processes in a computerized model. By "semantic relevance", we mean certain inferential relations among acquired beliefs which may facilitate information retrieval and practical reasoning under certain epistemic (...) constraints, e. g., the insufficiency of knowledge, the limitation of time budget, etc. However, traditional approaches to relevance—as for example, relevance logic, the Bayesian approach, as well as Description Logic—have failed to do justice to the foregoing constraints, and in this sense, they are not proper tools for solving the frame problem/relevance problem. As we will argue in this paper, Non-Axiomatic Reasoning System (NARS) can handle the frame problem in a more proper manner, because the resulting solution seriously takes epistemic constraints on cognition as a fundamental theoretical principle. (shrink)

Philosophy and statistics have studied two causal species, deterministic and probabilistic. There's a third species, however, hitherto unanalysed: underdeterministic causal phenomena, which are non-deterministic yet non-probabilistic. Here, I formulate a framework for modelling them. -/- Consider a simple case. If I go out, I may stumble into you but also may miss you. If I don’t go out, we won't meet. I go out. We meet. My going out is a cause of our encounter even if there was no determinate (...) probability of us meeting conditional on my going out. The cause is neither deterministic (it didn't necessitate the effect) nor probabilistic (the relevant conditional probabilities are undefined). Rather, it's underdeterministic: it raises the modal status of the effect from causally impossible to possible. -/- Here, I won't offer a theory of such token causes but develop the prerequisite for any such theory: the underdeterministic framework. The framework is like the deterministic structural-equations framework but with one consequential difference---an equation can return multiple values. This change allows me to define causal possibility and necessity, and corresponding notions of interventionist might- and would-counterfactuals. I also define conditional independence, which obeys the graphoid axioms, and prove that underdeterministic models satisfy the causal Markov condition. The framework can causally model situations that other frameworks cannot: decision-making under bounded uncertainty, games with multiple equilibria, infinite fair lotteries, and any other non-deterministic situation where indeterminacies are essentially non-probabilistic, or where we have a reason not to use probabilities. (shrink)

Numerous papers have investigated the transitivity principle of ‘better-than.’ A recent argument appeals to the principle of mereological dominance for transitivity. However, writers have not treated mereological dominance in much detail. This paper sets out to evaluate the generality of mereological dominance and its effectiveness in supporting the transitivity principle. I found that the mereological dominance principle is vulnerable to a counterexample based on Simpson’s Paradox. The thesis concludes that the mereological dominance principle should be revised in certain ways.

The evidence from randomized controlled trials (RCTs) is widely regarded as supplying the ‘gold standard’ in medicine—we may sometimes have to settle for other forms of evidence, but this is always epistemically second-best. But how well justified is the epistemic claim about the superiority of RCTs? This paper adds to my earlier (predominantly negative) analyses of the claims produced in favour of the idea that randomization plays a uniquely privileged epistemic role, by closely inspecting three related arguments from leading contributors (...) to the burgeoning field of probabilistic causality—Papineau, Cartwright and Pearl. It concludes that none of these further arguments supplies any practical reason for thinking of randomization as having unique epistemic power. IntroductionWhy the issue is of great practical importance—the ECMO casePapineau on the ‘virtues of randomization’Cartwright on causality and the ‘ideal’ randomized experimentPearl on randomization, nets and causesConclusion. (shrink)

This paper explores some issues concerning how we should think about interventions (in the sense of unconfounded manipulations) of "upper-level" variables in contexts in which these supervene on but are not identical with lower-level realizers. It is argued that we should reject the demand that interventions on upper-level variables must leave their lower-level realizers unchanged– a requirement that within an interventionist framework would imply that upper-level variables are causally inert. Instead an intervention on an upper-level variable at the same time (...) changes its lower-level realizer in a way that is consistent with the change in the upper- level variable. The lower-level realizer should not be regarded as a potential "confounder" of upper-level causal relations, as in "exclusionist" arguments. Several proposals for making this precise are considered and several pro-exclusionist arguments are criticized. (shrink)

This article defends the use of interventionist counterfactuals to elucidate causal and explanatory claims against criticisms advanced by James Bogen and Peter Machamer. Against Bogen, I argue that counterfactual claims concerning what would happen under interventions are meaningful and have determinate truth values, even in a deterministic world. I also argue, against both Machamer and Bogen, that we need to appeal to counterfactuals to capture the notions like causal relevance and causal mechanism. Contrary to what both authors suppose, counterfactuals are (...) not "unscientific" - a substantial tradition within statistics and the causal modelling literature makes heavy use of them. (shrink)

We use our knowledge of causal relationships to imagine possible events. We also use these relationships to look deep into the past and infer events that were not witnessed or to infer what can not be directly seen in the present. Knowledge of causal relationships allows us to go beyond the here and now. This chapter introduces a new theoretical framework for how this very basic concept might be mentally represented. It proposes an epistemological theory of causation — that is, (...) a theory that specifies the nature of people's knowledge of causation, the notion of causation used in everyday language and reasoning. (shrink)

Fodor's thinking on modularity has been influential throughout a range of the areas studying cognition, chiefly as a prod for positive work on modularity and domain-specificity. In _The Mind Doesn't Work That Way_, Fodor has developed the dark message of _The Modularity of Mind_ regarding the limits to modularity and computational analyses. This paper offers a critical assessment of Fodor's scepticism with an eye to highlighting some broader issues in play, including the nature of computation and the role of recent (...) empirical developments in the cognitive sciences in assessing Fodor's position. (shrink)

Systems medicine is a promising new paradigm for discovering associations, causal relationships and mechanisms in medicine. But it faces some tough challenges that arise from the use of big data: in particular, the problem of how to integrate evidence and the problem of how to structure the development of models. I argue that objective Bayesian models offer one way of tackling the evidence integration problem. I also offer a general methodology for structuring the development of models, within which the objective (...) Bayesian approach fits rather naturally. (shrink)

In recent years, realism?particularly critical realism?has become an important philosophical and methodological foundation for social science. A key feature is that of natural necessity, but this coexists alongside an acceptance of contingency in the social world. I argue in this paper that there cannot be any natural necessity in the social world, but rather the real nature of the social world is that it is contingent. This need not lead to an abandonment of realism, and indeed I argue that a (...) contingent form of realism allows us to bring ontological probability back into realism. (shrink)

Social Epistemology, Volume 25, Issue 2, Page 167-172, April 2011.

Concerning a conversation about grounding between Philo, a quizzical maverick, and Cleanthes, a studious devotee of the very latest trends in metaphysics. Whereas Cleanthes enthuses about grounding, Philo counsels methodological caution and greater immersion in actual scientific practice.

Some have argued that our intuitive reactions to a number of cases of moral responsibility can only be preserved at the expense of a unified account of moral responsibility for acts and omissions. I argue against this conclusion, proposing that a plausible condition on responsibility, the Causal Condition can, when properly elaborated, justify the relevant intuitive data.

Making a Difference: Essays on the Philosophy of Causation Edited by BeebeeHelen, HitchcockChristopher and PriceHuwOxford University Press, 2017. xii + 336 pp.

A cause may influence its effect via multiple paths. Paradigmatically (Hesslow [1974]), taking birth control pills both decreases one’s risk of thrombosis by preventing pregnancy and increases it by producing a blood chemical. Building on Pearl ([2001]), I explicate the notion of a path-specific effect. Roughly, a path-specific effect of C on E via path P is the degree to which a change in C would change E were they to be transmitted only via P. Facts about such effects may (...) be gleaned from the structural equations commonly used to represent the causal relationships among variables. I contrast my analysis of the Hesslow case with those given by theorists of probabilistic causality, who mistakenly link it to issues of causal heterogeneity, token-causation and indeterminism. The reason probabilistic theories misdiagnose this case is that they pay inadequate attention to the structural relationships among variables. (shrink)

Some recent accounts of constitutive relevance have identified mechanism components with entities that are causal intermediaries between the input and output of a mechanism. I argue that on such accounts there is no distinctive inter-level form of mechanistic explanation and that this highlights an absence in the literature of a compelling argument that there are such explanations. Nevertheless, the entities that these accounts call ‘components’ do play an explanatory role. Studying causal intermediaries linking variables Xand Y provides knowledge of the (...) counterfactual conditions under which X will continue to bring about Y. This explanatory role does not depend on whether intermediate variables count as components. The question of whether there are distinctively mechanistic explanations remains open. (shrink)

Some recent accounts of constitutive relevance have identified mechanism components with entities that are causal intermediaries between the input and output of a mechanism. I argue that on such accounts there is no distinctive inter-level form of mechanistic explanation and that this highlights an absence in the literature of a compelling argument that there are such explanations. Nevertheless, the entities that these accounts call ‘components’ do play an explanatory role. Studying causal intermediaries linking variables Xand Y provides knowledge of the (...) counterfactual conditions under which X will continue to bring about Y. This explanatory role does not depend on whether intermediate variables count as components. The question of whether there are distinctively mechanistic explanations remains open. (shrink)

Within the causal modeling literature, debates about the Causal Faithfulness Condition have concerned whether it is probable that the parameters in causal models will have values such that distinct causal paths will cancel. As the parameters in a model are fixed by the probability distribution over its variables, it is initially puzzling what it means to assign probabilities to these parameters. I propose that to assign a probability to a parameter in a model is to treat that parameter as a (...) function of a variable in an augmented model. By combining this proposal with widely adopted principles regarding which variables must be included in a model, I argue that the various proposed counterexamples to CFC involving coordinated parameters are not genuine counterexamples. I then consider the cases in which CFC fails due not to coordination, but by coincidence, and propose explanatory and predictive bases for ruling out such coincidences without presuming that they are improbable. The aim of the proposed defenses is not to show that CFC never fails, but rather to argue that its use in a particular context may be defended using general modeling assumptions rather than by relying on claims about how often it fails. (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)

I argue that the best interpretation of the general theory of relativity has need of a causal entity, and causal structure that is not reducible to light cone structure. I suggest that this causal interpretation of GTR helps defeat a key premise in one of the most popular arguments for causal reductionism, viz., the argument from physics.

Necessity and sufficiency are the building blocks of all successful explanations. Yet despite their importance, these notions have been conceptually underdeveloped and inconsistently applied in explainable artificial intelligence, a fast-growing research area that is so far lacking in firm theoretical foundations. In this article, an expanded version of a paper originally presented at the 37th Conference on Uncertainty in Artificial Intelligence, we attempt to fill this gap. Building on work in logic, probability, and causality, we establish the central role of (...) necessity and sufficiency in XAI, unifying seemingly disparate methods in a single formal framework. We propose a novel formulation of these concepts, and demonstrate its advantages over leading alternatives. We present a sound and complete algorithm for computing explanatory factors with respect to a given context and set of agentive preferences, allowing users to identify necessary and sufficient conditions for desired outcomes at minimal cost. Experiments on real and simulated data confirm our method’s competitive performance against state of the art XAI tools on a diverse array of tasks. (shrink)

In their recent book, Is Inequality Bad for Our Health?, Daniels, Kennedy, and Kawachi claim that to “act justly in health policy, we must have knowledge about the causal pathways through which socioeconomic (and other) inequalities work to produce differential health outcomes.” One of the central problems with this approach is its dependency on “knowledge about the causal pathways.” A widely held belief is that the randomized clinical trial (RCT) is, and ought to be the “gold standard” of evaluating the (...) causal efficacy of interventions. However, often the only data available are non-experimental, observational data. For such data, the necessary randomization is missing. Because the randomization is missing, it seems to follow that it is not possible to make epistemically warranted claims about the causal pathways. Although we are not sanguine about the difficulty in using observational data to make warranted causal claims, we are not as pessimistic as those who believe that the only warranted causal claims are claims based on data from (idealized) RCTs. We argue that careful, thoughtful study design, informed by expert knowledge, that incorporates propensity score matching methods in conjunction with instrumental variable analyses, provides the possibility of warranted causal claims using observational data. (shrink)

Sustainability is an important topic for understanding and developing our society. For scholars who want their academic contributions to have an impact, sustainability is important for our conceptual systems. Because our conceptual systems share similarities with our social systems, we may investigate their characteristics to gain insight into how both may be achieved or at least understood. Theories of the humanities as well as the social/behavioral sciences are changing very rapidly. They are fragile and few seem to have any longevity. (...) At the same time, the theoretical base does not seem to be “advancing.” They are not supporting highly effective results in the real world, so we continue to have seemingly insolvable problems such as crime, war, and poverty. This may be because academia has become inward-focused or, in Luhmann’s terminology, autonomous from the outside world. In seeking to understand how to develop more sustainable theories we found that the concept of sustainability is contested. And, in the process of comparing the sustainability of social systems to the sustainability of theories, we came to realize that neither perspective is viable. Drawing on Luhmann’s insights on the interdependence of theories and society, we came to realize that the two exist in a coevolutionary relationship. Importantly, we present an approach for measuring that evolution and suggest directions for accelerating the coevolutionary advance of society and science. (shrink)

We argue that any account of mental disorders that meets the desideratum of assigning causal efficacy to mental disorders faces the so-called “causal exclusion problem”. We argue that fully reductive accounts solve this problem but run into the problem of multiple realizability. Recently advocated symptom-network approaches avoid the problem of multiple realizability, but they also run into the causal exclusion problem. Based on a critical analysis of these accounts, we will present our own account according to which mental disorders are (...) dispositional properties that are token-identical to physical properties. More specifically, they are analyzed as dispositions to cause the specific set of symptoms. We argue that our account is not only able to account for multiple realizability without running into the causal exclusion problem, but that it also allows for the integration of very different factors into the description and explanation of mental disorders, such as neurological and neurochemical factors on the one side and social and cultural factors on the other. It thereby gives the psychiatric level of causal explanation autonomy while securing the causal efficacy of mental disorders in a causally closed physical world. (shrink)

A notion of delegated causality is introduced here. This subtle kind of causality is dual to interventional causality. Delegated causality elucidates the causal role of dynamical systems at the “edge of chaos”, explicates evident cases of downward causation, and relates emergent phenomena to Gödel’s incompleteness theorem. Apparently rich implications are noticed in biology and Chinese philosophy. The perspective of delegated causality supports cognitive interpretations of self-organization and evolution.

We review publications in automated scientific discovery using deep learning, with the aim of shedding light on problems with strong connections to philosophy of science, of physics in particular. We show that core issues of philosophy of science, related, notably, to the nature of scientific theories; the nature of unification; and of causation loom large in scientific deep learning. Therefore, advances in deep learning could, and ideally should, have impact on philosophy of science, and vice versa. We suggest lines of (...) further research, and highlight the role ‘theory-based’ AI could have in future developments of the field. (shrink)