How to tell causes from effects: Kant’s causal theory of time and modern approaches

I attempt a reconstruction of Kant’s version of the causal theory of time that makes it appear coherent. Two problems are at issue. The first concerns Kant’s reference to reciprocal causal influence for characterizing simultaneity. This approach is criticized by pointing out that Kant’s procedure involves simultaneous counterdirected processes—which seems to run into circularity. The problem can be defused by drawing on instantaneous processes such as the propagation of gravitation in Newtonian mechanics. Another charge of circularity against Kant’s causal theory was leveled by Schopenhauer. His objection was that Kant’s approach is invalidated by the failure to deliver non-temporal criteria for distinguishing between causes and effects. I try to show that the modern causal account has made important progress toward a successful resolution of this difficulty. The fork asymmetry, as based on Reichenbach’s principle of the common cause, provides a means for the distinction between cause and effect that is not based on temporal order (if some preconditions are realized).     

Kuhn, the correspondence theory of truth and coherentist epistemology

Kuhn argued against both the correspondence theory of truth and convergent realism. Although he likely misunderstood the nature of the correspondence theory, which it seems he wrongly believed to be an epistemic theory, Kuhn had an important epistemic point to make. He maintained that any assessment of correspondence between beliefs and reality is not possible, and therefore, the acceptance of beliefs and the presumption of their truthfulness has to be decided on the basis of other criteria. I will show that via Kuhn’s suggested epistemic values, specifically via problem-solving, his philosophy can be incorporated into a coherentist epistemology. Further, coherentism is, in principle, compatible with convergent realism. However, an argument for increasing likeness to truth requires appropriate historical continuity. Kuhn maintained that the history of science is full of discontinuity, and therefore, the historical condition of convergent realism is not satisfied.     

Causal implications: The linkage between time series and econometric modelling

Construction of causally and structurally adequate simultaneous equations models can be accomplished by determining causal relations between potential variables and balancing these statistically derived inferences with economic theory to relate behavioural or technological forces among the variables. An appropriate lag structure for each of the equations can be determined by a two step multiple transfer function approach involving reduced form equations. Testing the specification of already existing simultaneous equations models is done by constructing multiple transfer function models of the reduced form equations of the simultaneous equations models which permit incorporation of lead cross correlations.     

The realizers and vehicles of mental representation

The neural vehicles of mental representation play an explanatory role in cognitive psychology that their realizers do not. Cognitive psychology individuates neural structures as representational vehicles in terms of the specific causal properties to which cognitive mechanisms are sensitive. Explanations that appeal to properties of vehicles can capture generalisations which are not available at the level of their neural realizers. In this paper, I argue that the individuation of realizers as vehicles restricts the sorts of explanations in which they can participate. I illustrate this with reference to Rupert’s (2011) claim that representational vehicles can play an explanatory role in psychology in virtue of their quantity or proportion. I propose that such quantity-based explanatory claims can apply only to realizers and not to vehicles, in virtue of the particular causal role that vehicles play in psychological explanations.     

Process tracing in political science: What's the story?

Methodologists in political science have advocated for causal process tracing as a way of providing evidence for causal mechanisms. Recent analyses of the method have sought to provide more rigorous accounts of how it provides such evidence. These accounts have focused on the role of process tracing for causal inference and specifically on the way it can be used with case studies for testing hypotheses. While the analyses do provide an account of such testing, they pay little attention to the narrative elements of case studies. I argue that the role of narrative in case studies is not merely incidental. Narrative does cognitive work by both facilitating the consideration of alternative hypotheses and clarifying the relationship between evidence and explanation. I consider the use of process tracing in a particular case (the Fashoda Incident) in order to illustrate the role of narrative. I argue that process tracing contributes to knowledge production in ways that the current focus on inference tends to obscure.     

Linear structures,causal sets and topology

Causal set theory and the theory of linear structures (which has recently been developed by Tim Maudlin as an alternative to standard topology) share some of their main motivations. In view of that, I raise and answer the question how these two theories are related to each other and to standard topology. I show that causal set theory can be embedded into Maudlin׳s more general framework and I characterise what Maudlin׳s topological concepts boil down to when applied to discrete linear structures that correspond to causal sets. Moreover, I show that all topological aspects of causal sets that can be described in Maudlin׳s theory can also be described in the framework of standard topology. Finally, I discuss why these results are relevant for evaluating Maudlin׳s theory. The value of this theory depends crucially on whether it is true that (a) its conceptual framework is as expressive as that of standard topology when it comes to describing well-known continuous as well as discrete models of spacetime and (b) it is even more expressive or fruitful when it comes to analysing topological aspects of discrete structures that are intended as models of spacetime. On one hand, my theorems support (a). The theory is rich enough to incorporate causal set theory and its definitions of topological notions yield a plausible outcome in the case of causal sets. On the other hand, the results undermine (b). Standard topology, too, has the conceptual resources to capture those topological aspects of causal sets that are analysable within Maudlin׳s framework. This fact poses a challenge for the proponents of Maudlin׳s theory to prove it fruitful.     

Mechanisms,the interventionist theory,and the ability to use causal relationships

In the area of social science, in particular, although we have developed methods for reliably discovering the existence of causal relationships, we are not very good at using these to design effective social policy. Cartwright argues that in order to improve our ability to use causal relationships, it is essential to develop a theory of causation that makes explicit the connections between the nature of causation, our best methods for discovering causal relationships, and the uses to which these are put. I argue that Woodward's interventionist theory of causation is uniquely suited to meet Cartwright's challenge. More specifically, interventionist mechanisms can provide the bridge from ‘hunting causes’ to ‘using them’, if interventionists (i) tell us more about the nature of these mechanisms, and (ii) endorse the claim that it is these mechanisms—or whatever constitutes them—that make causal claims true. I illustrate how having an understanding of interventionist mechanisms can allow us to put causal knowledge to use via a detailed example from organic chemistry.     

Model robustness as a confirmatory virtue: The case of climate science

I propose a distinct type of robustness, which I suggest can support a confirmatory role in scientific reasoning, contrary to the usual philosophical claims. In model robustness, repeated production of the empirically successful model prediction or retrodiction against a background of independently-supported and varying model constructions, within a group of models containing a shared causal factor, may suggest how confident we can be in the causal factor and predictions/retrodictions, especially once supported by a variety of evidence framework. I present climate models of greenhouse gas global warming of the 20th Century as an example, and emphasize climate scientists' discussions of robust models and causal aspects. The account is intended as applicable to a broad array of sciences that use complex modeling techniques.     

Explanation,understanding, and unrealistic models

How can false models be explanatory? And how can they help us to understand the way the world works? Sometimes scientists have little hope of building models that approximate the world they observe. Even in such cases, I argue, the models they build can have explanatory import. The basic idea is that scientists provide causal explanations of why the regularity entailed by an abstract and idealized model fails to obtain. They do so by relaxing some of its unrealistic assumptions. This method of ‘explanation by relaxation’ captures the explanatory import of some important models in economics. I contrast this method with the accounts that Daniel Hausman and Nancy Cartwright have provided of explanation in economics. Their accounts are unsatisfactory because they require that the economic model regularities obtain, which is rarely the case. I go on to argue that counterfactual regularities play a central role in achieving ‘understanding by relaxation.’ This has a surprising implication for the relation between explanation and understanding: Achieving scientific understanding does not require the ability to explain observed regularities.     

Selection of the relevant information set for predictive relationships analysis between time series

In time series analysis, a vector Y is often called causal for another vector X if the former helps to improve the k‐step‐ahead forecast of the latter. If this holds for k=1, vector Y is commonly called Granger‐causal for X . It has been shown in several studies that the finding of causality between two (vectors of) variables is not robust to changes of the information set. In this paper, using the concept of Hilbert spaces, we derive a condition under which the predictive relationships between two vectors are invariant to the selection of a bivariate or trivariate framework. In more detail, we provide a condition under which the finding of causality (improved predictability at forecast horizon 1) respectively non‐causality of Y for X is unaffected if the information set is either enlarged or reduced by the information in a third vector Z . This result has a practical usefulness since it provides a guidance to validate the choice of the bivariate system { X , Y } in place of { X , Y , Z }. In fact, to test the ‘goodness’ of { X , Y } we should test whether Z Granger cause X not requiring the joint analysis of all variables in { X , Y , Z }. Copyright © 2002 John Wiley & Sons, Ltd.     

The ‘Darwin-Marx correspondence’: a correction and revision

The logical links between the Judaeo-Christian doctrine of creation and the practice of natural philosophy on the one hand, and the rejection of belief in demonic agency on the other, were made explicit in the seventeenth century by, among others, Balthasar Bekker (1634–98), whose ideas I argue to have been not without influence. In section 1, I present the accounts of three historians of the opposition to belief in witchcraft and of the decline of the witch-persecution, Hugh Trevor-Roper, Keith Thomas, and Brian Easlea. In section 2, I maintain that Bekker has been underestimated both by Trevor-Roper and by Easlea. In section 3, I investigate more generally some of the connections between the new natural philosophy and belief in supernatural interventions, cast doubt on the view that rejection of belief in witchcraft and the devil requires rejection of belief in creation, and thus supplement or qualify the accounts of Trevor-Roper, Thomas, and Easlea of why belief in witchcraft faded away.     

Supervenience and (non-modal) reductionism in Leibniz’s philosophy of time

It has recently been suggested that, for Leibniz, temporal facts globally supervene on causal facts, with the result that worlds differing with respect to their causal facts can be indiscernible with respect to their temporal facts. Such an interpretation is at variance with more traditional readings of Leibniz’s causal theory of time, which hold that Leibniz reduces temporal facts to causal facts. In this article, I argue against the global supervenience construal of Leibniz’s philosophy of time. On the view of Leibniz defended here, he adopts a non-modal reduction of time to events, a form of reductionism that entails a strong covariation between a world’s temporal facts and its causal facts. Consequently, worlds discernible with respect to their temporal facts must be discernible with respect to their causal facts, and worlds discernible with respect to their causal facts must be discernible with respect to their temporal facts. This position strongly favors the standard identificatory reduction of time to causation often imputed to Leibniz.     

Saving the mutual manipulability account of constitutive relevance

Constitutive mechanistic explanations are said to refer to mechanisms that constitute the phenomenon-to-be-explained. The most prominent approach of how to understand this relation is Carl Craver's mutual manipulability approach (MM) to constitutive relevance. Recently, MM has come under attack (Baumgartner and Casini 2017; Baumgartner and Gebharter 2015; Harinen 2014; Kästner 2017; Leuridan 2012; Romero 2015). It is argued that MM is inconsistent because, roughly, it is spelled out in terms of interventionism (which is an approach to causation), whereas constitutive relevance is said to be a non-causal relation. In this paper, I will discuss a strategy of how to resolve this inconsistency—so-called fat-handedness approaches (Baumgartner and Casini 2017; Baumgartner and Gebharter 2015; Romero 2015). I will argue that these approaches are problematic. I will present a novel suggestion for how to consistently define constitutive relevance in terms of interventionism. My approach is based on a causal interpretation of manipulability in terms of causal relations between the mechanism's components and what I will call temporal EIO-parts of the phenomenon. Still, this interpretation accounts for the fundamental difference between constitutive relevance and causal relevance.     

Big data and prediction: Four case studies

Has the rise of data-intensive science, or ‘big data’, revolutionized our ability to predict? Does it imply a new priority for prediction over causal understanding, and a diminished role for theory and human experts? I examine four important cases where prediction is desirable: political elections, the weather, GDP, and the results of interventions suggested by economic experiments. These cases suggest caution. Although big data methods are indeed very useful sometimes, in this paper's cases they improve predictions either limitedly or not at all, and their prospects of doing so in the future are limited too.     

Aspects of Quantum Non-Locality II: Superluminal Causation and Relativity

In a preceding paper, I studied the significance of Jarrett's and Shimony's analyses of ‘factorisability’ into ‘parameter independence’ and ‘outcome independence’ for clarifying the nature of non-locality in quantum phenomena. I focused on four types of non-locality; superluminal signalling, action-at-a-distance, non-separability and holism. In this paper, I consider a fifth type of non-locality: superluminal causation according to ‘logically weak’ concepts of causation, where causal dependence requires neither action nor signalling. I conclude by considering the compatibility of non-factorisable theories with relativity theory. In this connection, I pay special attention to the difficulties that superluminal causation raises in relativistic spacetime. My main findings in this paper are: first, parameter-dependent and outcome-dependent theories both involve superluminal causal connections between outcomes and between settings and outcomes. Second, while relativistic deterministic parameter-dependent theories seem impossible on pain of causal paradoxes, relativistic indeterministic parameter-dependent theories are not subjected to the same challenge. Third, current relativistic non-factorisable theories seem to have some rather unattractive characteristics.     

In defence of story-telling

We argue that narratives are central to the success of historical reconstruction. Narrative explanation involves tracing causal trajectories across time. The construction of narrative, then, often involves postulating relatively speculative causal connections between comparatively well-established events. But speculation is not always idle or harmful: it also aids in overcoming local underdetermination by forming scaffolds from which new evidence becomes relevant. Moreover, as our understanding of the past's causal milieus become richer, the constraints on narrative plausibility become increasingly strict: a narrative's admissibility does not turn on mere logical consistency with background data. Finally, narrative explanation and explanation generated by simple, formal models complement one another. Where models often achieve isolation and precision at the cost of simplification and abstraction, narratives can track complex changes in a trajectory over time at the cost of simplicity and precision. In combination both allow us to understand and explain highly complex historical sequences.