Interventionism and Higher-level Causation

Several authors have recently claimed that the notorious causal exclusion problem, according to which higher-level causes are threatened with causal pre-emption by lower-level causes, can be avoided if causal relevance is understood in terms of Woodward's interventionist account of causation. They argue that if causal relevance is defined in interventionist terms, there are cases where only higher-level properties, but not the lower-level properties underlying them, qualify as causes of a certain effect. In this article, I show that the line of reasoning supposed to establish this claim does not succeed and that interventionism is not better capable of dealing with higher-level causal claims than other accounts of causation. According to Woodward, higher-level causal claims are nonetheless more adequate than lower-level ones if they describe a realization-independent dependency relationship and, hence, meet the requirement that causes should be proportional to their effects. I argue, however, that combining interventionism with proportionality considerations raises difficulties and that, therefore, Woodward's account does not vindicate higher-level causation.     

Physics and the Human Face of Causation

Many contemporary philosophers of physics (and philosophers of science more generally) follow Bertrand Russell in arguing that there is no room for causal notions in physics. Causation, as James Woodward has put it, has a ‘human face’, which makes causal notions sit ill with fundamental theories of physics. In this paper I examine a range of anti-causal arguments and show that the human face of causation is the face of scientific representations much more generally. Physics, like other sciences, is deeply permeated with causal reasoning.     

Probabilistic single function dual process theory and logic programming as approaches to non-monotonicity in human vs. artificial reasoning

In this paper, it is argued that single function dual process theory is a more credible psychological account of non-monotonicity in human conditional reasoning than recent attempts to apply logic programming (LP) approaches in artificial intelligence to these data. LP is introduced and among other critiques, it is argued that it is psychologically unrealistic in a similar way to hash coding in the classicism vs. connectionism debate. Second, it is argued that causal Bayes nets provide a framework for modelling probabilistic conditional inference in System 2 that can deal with patterns of inference LP cannot. Third, we offer some speculations on how the cognitive system may avoid problems for System 1 identified by Fodor in 1983. We conclude that while many problems remain, the probabilistic single function dual processing theory is to be preferred over LP as an account of the non-monotonicity of human reasoning.     

Quasi-Realism and Inductive Scepticism in Hume’s Theory of Causation

Interpreters of Hume on causation consider that an advantage of the ‘quasi-realist’ reading is that it does not commit him to scepticism or to an error theory about causal reasoning. It is unique to quasi-realism that it maintains this positive epistemic result together with a rejection of metaphysical realism about causation: the quasi-realist supplies an appropriate semantic theory in order to justify the practice of talking ‘as if’ there were causal powers in the world. In this paper, I problematise the quasi-realist reading of Hume on causation by showing how quasi-realism does not speak to inductive scepticism. I also offer evidence that Hume takes inductive scepticism to result from his theory of causation, and that his scepticism is tied to his rejection of metaphysical causal realism.     

A probabilistic theory of second order causation

Larry Wright and others have advanced causal accounts of functional explanation, designed to alleviate fears about the legitimacy of such explanations. These analyses take functional explanations to describe second order causal relations. These second order relations are conceptually puzzling. I present an account of second order causation from within the framework of Eells' probabilistic theory of causation; the account makes use of the population-relativity of causation that is built into this theory.     

Is Dispositional Causation Just Mutual Manifestation?

Dispositional properties are often referred to as ‘causal powers’, but what does dispositional causation amount to? Any viable theory must account for two fundamental aspects of the metaphysics of causation – the causal complexity and context sensitivity of causal interactions. The theory of mutual manifestations attempts to do so by locating the complexity and context sensitivity within the nature of dispositions themselves. But is this theory an acceptable first step towards a viable theory of dispositional causation? This paper argues that the reconceptualization that the theory entails comes at too high a price, and is an unnecessary step in the wrong direction: these two central aspects concerning the metaphysics of causation can and should be accounted for in a dispositional account of causation without it.     

Intervention,determinism, and the causal minimality condition

We clarify the status of the so-called causal minimality condition in the theory of causal Bayesian networks, which has received much attention in the recent literature on the epistemology of causation. In doing so, we argue that the condition is well motivated in the interventionist (or manipulability) account of causation, assuming the causal Markov condition which is essential to the semantics of causal Bayesian networks. Our argument has two parts. First, we show that the causal minimality condition, rather than an add-on methodological assumption of simplicity, necessarily follows from the substantive interventionist theses, provided that the actual probability distribution is strictly positive. Second, we demonstrate that the causal minimality condition can fail when the actual probability distribution is not positive, as is the case in the presence of deterministic relationships. But we argue that the interventionist account still entails a pragmatic justification of the causal minimality condition. Our argument in the second part exemplifies a general perspective that we think commendable: when evaluating methods for inferring causal structures and their underlying assumptions, it is relevant to consider how the inferred causal structure will be subsequently used for counterfactual reasoning.     

Causal reasoning

The main focus of this paper is the question as to what it is for an individual to think of her environment in terms of a concept of causation, or causal concepts, in contrast to some more primitive ways in which an individual might pick out or register what are in fact causal phenomena. I show how versions of this question arise in the context of two strands of work on causation, represented by Elizabeth Anscombe and Christopher Hitchcock, respectively. I then describe a central type of reasoning that, I suggest, a subject has to be able to engage in, if we are to credit her with causal concepts. I also point out that this type of reasoning turns on the idea of a physical connection between cause and effect, as articulated in recent singularist approaches of causation.     

Causal concepts and temporal ordering

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.

     

Natural Selection: A Case for the Counterfactual Approach

This paper investigates the conception of causation required in order to make sense of natural selection as a causal explanation of changes in traits or allele frequencies. It claims that under a counterfactual account of causation, natural selection is constituted by the causal relevance of traits and alleles to the variation in traits and alleles frequencies. The “statisticalist” view of selection (Walsh, Matthen, Ariew, Lewens) has shown that natural selection is not a cause superadded to the causal interactions between individual organisms. It also claimed that the only causation at work is those aggregated individual interactions, natural selection being only predictive and explanatory, but it is implicitly committed to a process-view of causation. I formulate a counterfactual construal of the causal statements underlying selectionist explanations, and show that they hold because of the reference they make to ecological reliable factors. Considering case studies, I argue that this counterfactual view of causal relevance proper to natural selection captures more salient features of evolutionary explanations than the statisticalist view, and especially makes sense of the difference between selection and drift. I eventually establish equivalence between causal relevance of traits and natural selection itself as a cause.     

The interventionist account of causation and the basing relation

It is commonplace to distinguish between propositional justification (having good reasons for believing p) and doxastic justification (believing p on the basis of those good reasons).One necessary requirement for bridging the gap between S??s merely having propositional justification that p and S??s having doxastic justification that p is that S base her belief that p on her reasons (propositional justification).A plausible suggestion for what it takes for S??s belief to be based on her reasons is that her reasons must contribute causally to S??s having that belief. Though this suggestion is plausible, causal accounts of the basing relation that have been proposed have not fared well. In particular, cases involving causal deviancy and cases involving over-determination have posed serious problems for causal accounts of the basing relation. Although previous causal accounts of the basing relation seem to fall before these problems, it is possible to construct an acceptable causal account of the basing relation. That is, it is possible to construct a causal account of the basing relation that not only fits our intuitions about doxastic justification in general, but also is not susceptible to the problems posed by causal deviancy and causal over-determination. The interventionist account of causation provides the tools for constructing such an account. My aim is to make use of the insights of the interventionist account of causation to develop and defend an adequate causal account of the basing relation.     

The causal asymmetry

It is hypothesized that there is a pervasive and fundamental bias in humans' understanding of physical causation: Once the roles of cause and effect are assigned to objects in interactions, people tend to overestimate the strength and importance of the causal object and underestimate that of the effect object in bringing about the outcome. This bias is termed the causal asymmetry. Evidence for this bias is reviewed in several domains, including visual impressions of causal relations, reasoning about Newton's third law in naive physics problems, concepts underlying linguistic expressions of causality, and research in causal judgment from contingency information. Although there might be an equivalent to the causal asymmetry in the domain of social causality, there are too many uncertainties in the evidence for conclusions to be drawn.     

A computational model for solving problems from the Raven’s Progressive Matrices intelligence test using iconic visual representations

We describe a computational model for solving problems from Raven’s Progressive Matrices (RPM), a family of standardized intelligence tests. Existing computational models for solving RPM problems generally reason over amodal propositional representations of test inputs. However, there is considerable evidence that humans can also apply imagery-based reasoning strategies to RPM problems, in which processes rooted in perception operate over modal representations of test inputs. In this paper, we present the “affine model,” a computational model that simulates modal reasoning by using iconic visual representations together with affine and set transformations over these representations to solve a given RPM problem. Various configurations of the affine model successfully solve between 33 and 38 of the 60 problems on the Standard Progressive Matrices, which matches levels of performance for typically developing 9- to 11-year-old children. This suggests that, for at least a sizeable subset of RPM problems, it is not always necessary to extract amodal symbols in order to arrive at the correct answer, and iconic visual representations constitute a sufficient form of representation to successfully solve these problems. We intend for the affine model to serve as a complementary computational account to existing propositional models, which together may provide an integrated, dual-process account of human problem solving on the RPM.     

Activity-Based Accounts of Mechanism and the Threat of Polygenic Effects

Accounts of ontic explanation have often been devised so as to provide an understanding of mechanism and of causation. Ontic accounts differ quite radically in their ontologies, and one of the latest additions to this tradition proposed by Peter Machamer, Lindley Darden and Carl Craver reintroduces the concept of activity. In this paper I ask whether this influential and activity-based account of mechanisms is viable as an ontic account. I focus on polygenic scenarios—scenarios in which the causal truths depend on more than one cause. The importance of polygenic causation was noticed early on by Mill (1893). It has since been shown to be a problem for both causal-law approaches to causation (Cartwright 1983) and accounts of causation cast in terms of capacities (Dupré 1993; Glennan 1997, pp. 605–626). However, whereas mechanistic accounts seem to be attractive precisely because they promise to handle complicated causal scenarios, polygenic causation needs to be examined more thoroughly in the emerging literature on activity-based mechanisms. The activity-based account proposed in Machamer et al. (2000, pp. 1–25) is problematic as an ontic account, I will argue. It seems necessary to ask, of any ontic account, how well it performs in causal situations where—at the explanandum level of mechanism—no activity occurs. In addition, it should be asked how well the activity-based account performs in situations where there are too few activities around to match the polygenic causal origin of the explanandum. The first situation presents an explanandum-problem and the second situation presents an explanans-problem—I will argue—both of which threaten activity-based frameworks.     

Epistemic normativity in Kant's “Second Analogy”

In the “Second Analogy,” Kant argues that, unless mental contents involve the concept of causation, they cannot represent an objective temporal sequence. According to Kant, deploying the concept of causation renders a certain temporal ordering of representations necessary, thus enabling objective representational purport. One exegetical question that remains controversial is this: how, and in what sense, does deploying the concept of cause render a certain ordering of representations necessary? I argue that this necessitation is a matter of epistemic normativity: with certain causal presuppositions in place, the individual is obliged to make a judgment with certain temporal contents, on pain of irrationality. To make this normatively obligatory judgment, the subject must place her perceptual representations in a certain order. This interpretation fits Kant's text, his argumentative aims, and his broader views about causal inference, better than rival interpretations can. This result has important consequences for the ongoing debate over the role of normativity in Kant's philosophy of mind.     

Analyzing Machine-Learned Representations: A Natural Language Case Study

As modern deep networks become more complex, and get closer to human-like capabilities in certain domains, the question arises as to how the representations and decision rules they learn compare to the ones in humans. In this work, we study representations of sentences in one such artificial system for natural language processing. We first present a diagnostic test dataset to examine the degree of abstract composable structure represented. Analyzing performance on these diagnostic tests indicates a lack of systematicity in representations and decision rules, and reveals a set of heuristic strategies. We then investigate the effect of training distribution on learning these heuristic strategies, and we study changes in these representations with various augmentations to the training set. Our results reveal parallels to the analogous representations in people. We find that these systems can learn abstract rules and generalize them to new contexts under certain circumstances—similar to human zero-shot reasoning. However, we also note some shortcomings in this generalization behavior—similar to human judgment errors like belief bias. Studying these parallels suggests new ways to understand psychological phenomena in humans as well as informs best strategies for building artificial intelligence with human-like language understanding.     

How Could There Be True Causal Claims Without There Being Special Causal Facts in the World?

Some philosophers of physics recently expressed their skepticism about causation (Norton 2003b, 2007). However, this is not new. The view that causation does not refer to any ontological category perhaps can be attributed to Hume, Kant and Russell. On the other hand, some philosophers (Wesley Salmon and Phil Dowe) view causation as a physical process and some others (Cartwright) view causation as making claims about capacities possessed by objects. The issue about the ontological status of causal claims involves issues concerning the ontological status of capacity, modality and dispositional claims. In this paper, my goal is to show that without engaging metaphysical debates about the ontological status of causal claims, it can be shown that we can objectively assign truth values to these statements. I argue that for causal claims to be objective we don't need to postulate the existence of special facts (specific to causal claims) in addition to ordinary physical facts described by physical theories. This, I think, is enough to justify the usefulness of this concept in certain branches (may be all) of science. Once this is achieved, there is no need to engage in unnecessary metaphysical debates. So, even if advanced physical theories don't mention this notion, causal reasoning can still be important in understanding the world not in the sense that science discovers special ontological category called causation but in the sense that we come to know certain facts about the world.     

The Ontology of Causal Process Theories

There is a widespread belief that the so-called process theories of causation developed by Wesley Salmon and Phil Dowe have given us an original account of what causation really is. In this paper, I show that this is a misconception. The notion of ??causal process?? does not offer us a new ontological account of causation. I make this argument by explicating the implicit ontological commitments in Salmon and Dowe??s theories. From this, it is clear that Salmon??s Mark Transmission Theory collapses to a counterfactual theory of causation, while the Conserved Quantity Theory collapses to David Fair??s phsyicalist reduction of causation.     

How agency can solve interventionism’s problem of circularity

Woodward’s interventionist theory of causation is beset by a problem of circularity: the analysis of causes is in terms of interventions, and the analysis of interventions is in terms of causes. This is not in itself an argument against the correctness of the analysis. But by requiring us to have causal knowledge prior to making any judgements about causation, Woodward’s theory does make it mysterious how we can ever start acquiring causal knowledge. We present a solution to this problem by showing how the interventionist notion of causation can be rationally generated from a more primitive agency notion of causation. The agency notion is easily and non-circularly applicable, but fails when we attempt to capture causal relations between non-actions. We show that the interventionist notion of causation serves as an appropriate generalisation of the agency notion. Furthermore, the causal judgements based on the latter generally remain true when rephrased in terms of the former, which allows one to use the causal knowledge gained by applying the agency notion as a basis for applying Woodward’s interventionist theory. We then present an overview of relevant empirical evidence from developmental psychology which shows that our proposed rational reconstruction lines up neatly with the actual development of causal reasoning in children. This gives additional plausibility to our proposal. The article thus provides a solution to one of the main problems of interventionism while keeping Woodward’s analysis intact.     

Causal reasoning and the diagnostic process

Background: Causal reasoning as a way to make a diagnosis seems convincing. Modern medicine depends on the search for causes of disease and it seems fair to assert that such knowledge is employed in diagnosis. Causal reasoning as it has been presented neglects to some extent the conception of multifactorial disease causes. Goal: The purpose of this paper is to analyze aspects of causation relevant for discussing causal reasoning in a diagnostic context. Procedures: The analysis will discuss different conceptions of causal reasoning in medical diagnosis, discriminating primarily between narrow causal diagnosis and more thorough causal explanation. The theory of causes as non-redundant factors in effective causal complexes is used as an analytical background. Causal explanations are performed according to different causal models. Such models of diagnosis are assumptions concerning structure and mechanisms, which cannot be directly or immediately observed. Conceptions and results of causal search strategies differ, according to the focus of the searcher. Causal reasoning is also seen in diagnosis in a more extensive meaning: the pin-pointing of factors responsible for the condition of the patient at any time during the course of disease. Conclusion: Causal reasoning and diagnosis go well in hand, especially if both concepts are widened. The theory of causes as non-redundant components in effective causal complexes, modulated by what is referred to as the stop problem and causal fields, is valuable for explaining the many aspects of causal reasoning in medical diagnosis.