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)

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)

The paper discusses from a metaphysical standpoint the nature of the dependence relation underpinning the talk of mutual action between material and spatiotemporal structures in general relativity. It is shown that the standard analyses of dependence in terms of causation or grounding are ill-suited for the general relativistic context. Instead, a non-standard analytical framework in terms of structural equation modeling is exploited, which leads to the conclusion that the kind of dependence encoded in the Einstein field equations is a novel (...) one. (shrink)

In this paper, we explain why the antecedent of a biscuit conditional is relevant to its consequent by extending Douvenʼs evidential support theory of conditionals making use of utilities. By this extension, we can also explain why a biscuit conditional gives rise to the inference that the consequence is (most likely) true. Finally, we account for the intuition that (indicative) biscuit sentences are false when the antecedent is false and allow for counterfactual biscuits.

Objects have dispositions. Dispositions are normally analyzed by providing a meaning to disposition ascriptions like ‘This piece of salt is soluble’. Philosophers like Carnap, Goodman, Quine, Lewis and many others have proposed analyses of such disposition ascriptions. In this paper we will argue with Quine that the proper analysis of ascriptions of the form ‘x is disposed to m ’, where ‘x’ denotes an object, ‘m’ a manifestation, and ‘C’ a condition, goes like this: ‘x is of natural kind k’, (...) and the generic ‘ks are m ’ is true. For the analysis of the generic, we propose an analysis in terms of causal powers: ‘ks have the causal power to m’. The latter, in turn, is analyzed in a very precise way, making use of Pearl’s probabilistic graphical causal models. We will show how this natural kind-analysis improves on standard conditional analyses of dispositions by avoiding the standard counterexamples, and that it gives rise to precise observable criteria under which the disposition ascription is true. (shrink)

Cimpian et al. observed that we accept generic statements of the form ‘Gs are f’ on relatively weak evidence, but that if we are unfamiliar with group G and we learn a generic statement about it, we still treat it inferentially in a much stronger way: all Gs are f. This paper makes use of notions like ‘representativeness’, ‘contingency’ and ‘relative difference’ from psychology to provide a uniform semantics of generics that explains why people accept generics based on weak evidence. (...) The spirit of the approach has much in common with Leslie’s cognition-based ideas about generics, but the semantics will be grounded on a strengthening of Cohen’s relative readings of generic sentences. In contrast to Leslie and Cohen, we propose a uniform semantic analysis of generics. The basic intuition is that a generic of the form ‘Gs are f’ is true because f is typical for G, which means that f is valuably associated with G. We will make use of Kahneman and Tversky’s Heuristics and Biases approach, according to which people tend to confuse questions about probability with questions about representativeness, to explain pragmatically why people treat many generic statements inferentially in a much stronger way. (shrink)

In the philosophy of causation, manipulationist literature is broadly divided into agency and interventionist accounts. The division between these accounts is partially due to a dispute regarding the meaning of “manipulation”, which specifically questions, “Must one analyse manipulation by appealing to human agency?” This paper attempts to clarify the notion of manipulation and defends the thesis that agency theorists and interventionists analyse manipulation by appealing to human agency. However, following Collingwood's work, I argue that there are two ways to interpret (...) “human agency”. With an extended notion of agency in place, I reveal a pathway towards a consensus between the agency and interventionist accounts. Further, I highlight the alleged discrepancies between agency and interventionist accounts, including the issues of scope and objectivity, and suggest additional pathways towards a consensus. (shrink)

The appropriateness, or acceptability, of a conditional does not just ‘go with’ the corresponding conditional probability. A condition of dependence is required as well. In this paper a particular notion of dependence is proposed. It is shown that under both a forward causal and a backward evidential reading of the conditional, this appropriateness condition reduces to conditional probability under some natural circumstances. Because this is in particular the case for the so-called diagnostic reading of the conditional, this analysis might help (...) to explain some of Douven and Verbrugge’s empirical observations. (shrink)

According to Adams, the acceptability of an indicative conditional goes with the conditional probability of the consequent given the antecedent. However, some conditionals seem to be inappropriate, although their corresponding conditional probability is high. These are cases with a missing link between antecedent and consequent. Other conditionals are appropriate even though the conditional probability is low. Finally, we have the so-called biscuit conditionals. In this paper we will generalize analyses of Douven and others to account for the appropriateness of conditionals (...) in terms of evidential support. Our generalization involves making use of Value, or intensity. We will show how this generalization helps to account for biscuit conditionals and conditional threats and promises. Finally, a link is established between this analysis of conditionals and an analysis of generic sentences. (shrink)

The causal efficacy of a material system is usually thought to be produced by the law-like actions and interactions of its constituents. Here, a specific system is constructed and explained that produces a cause that cannot be understood in this way, but instead has novel and autonomous efficacy. The construction establishes a proof-of-feasibility of strong emergence. The system works by utilizing randomness in a targeted and cyclical way, and by relying on sustained evolution by natural selection. It is not vulnerable (...) to standard arguments against strong emergence, in particular ones that assume that the physical realm is causally closed. Moreover, it does not suffer from epiphenomenalism or causal overdetermination. The system uses only standard material components and processes, and is fully consistent with naturalism. It is discussed whether the emergent cause can still be viewed as ‘material’ in the way that term is commonly understood. (shrink)

Many generic sentences express stable inductive generalizations. Stable inductive generalizations are typically true for a causal reason. In this paper we investigate to what extent this is also the case for the generalizations expressed by generic sentences. More in particular, we discuss the possibility that many generic sentences of the form ‘ks have feature e’ are true because kind k have the causal power to ‘produce’ feature e. We will argue that such an analysis is quite close to a probabilistic (...) based analysis of generic sentences according to which ‘relatively many’ ks have feature e, and that, in fact, this latter type of analysis can be ‘grounded’ in terms of causal powers. We will argue, moreover, that the causal power analysis is sometimes preferred to a correlation-based analysis, because it takes the causal structure that gives rise to the probabilistic data into account. (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 present a probabilistic extension to active path analyses of token causation (Halpern & Pearl 2001, forthcoming; Hitchcock 2001). The extension uses the generalized notion of intervention presented in (Korb et al. 2004): we allow an intervention to set any probability distribution over the intervention variables, not just a single value. The resulting account can handle a wide range of examples. We do not claim the account is complete --- only that it fills an obvious gap in previous active-path approaches. (...) It still succumbs to recent counterexamples by Hiddleston (2005), because it does not explicitly consider causal processes. We claim three benefits: a detailed comparison of three active-path approaches, a probabilistic extension for each, and an algorithmic formulation. (shrink)

The investigation of probabilistic causality has been plagued by a variety of misconceptions and misunderstandings. One has been the thought that the aim of the probabilistic account of causality is the reduction of causal claims to probabilistic claims. Nancy Cartwright (1979) has clearly rebutted that idea. Another ill-conceived idea continues to haunt the debate, namely the idea that contextual unanimity can do the work of objective homogeneity. It cannot. We argue that only objective homogeneity in combination with a causal interpretation (...) of Bayesian networks can provide the desired criterion of probabilistic causality. (shrink)

It has been argued that supervenience generates unavoidable confounding problems for interventionist accounts of causation, to the point that we must choose between interventionism and supervenience. According to one solution, the dilemma can be defused by excluding non-causal determinants of an outcome as potential confounders. I argue that this solution undermines the methodological validity of causal tests. Moreover, we don’t have to choose between interventionism and supervenience in the first place. Some confounding problems are effectively circumvented by experimental designs routinely (...) employed in science. The remaining confounding issues concern the physical interpretation of variables and cannot be solved by choosing between interventionism and supervenience. (shrink)

The generality problem is one of the most pressing challenges for process reliabilism about justification. Thus far, one of the more promising responses is James Beebe’s tri-level statistical solution. Despite the initial plausibility of Beebe’s approach, the tri-level statistical solution has been shown to generate implausible justification verdicts on a variety of cases. Recently, Samuel Kampa has offered a new statistical solution to the generality problem. Kampa argues that the new statistical solution overcomes the challenges that undermined Beebe’s original statistical (...) solution. However, there’s good reason to believe that Kampa is mistaken. In this paper, I show that Kampa’s new statistical solution faces problems that are no less serious than the original objections to Beebe’s solution. Depending on how we interpret Kampa’s proposal, the new statistical solution either types belief-forming processes far too narrowly, or the new statistical solution fails to clarify the epistemic implications of reliabilism altogether. Either way, the new statistical solution fails to make substantive progress towards solving the generality problem. (shrink)

Early agreement within cognitive science on the topic of representation has now given way to a combination of positions. Some question the significance of representation in cognition. Others continue to argue in favor, but the case has not been demonstrated in any formal way. The present paper sets out a framework in which the value of representation use can be mathematically measured, albeit in a broadly sensory context rather than a specifically cognitive one. Key to the approach is the use (...) of Bayesian networks for modeling the distal dimension of sensory processes. More relevant to cognitive science is the theoretical result obtained, which is that a certain type of representational architecture is necessary for achievement of sensory efficiency. While exhibiting few of the characteristics of traditional, symbolic encoding, this architecture corresponds quite closely to the forms of embedded representation now being explored in some embedded/embodied approaches. It becomes meaningful to view that type of representation use as a form of information recovery. A formal basis then exists for viewing representation not so much as the substrate of reasoning and thought, but rather as a general medium for efficient, interpretive processing. (shrink)

This paper develops a descriptive and normative account of how people respond to testimony. It postulates a default pathway in which people more or less automatically respond to a claim by accepting it, as long as the claim made is consistent with their beliefs and the source is credible. Otherwise, people enter a reflective pathway in which they evaluate the claim based on its explanatory coherence with everything else they believe. Computer simulations show how explanatory coherence can be maximized in (...) real-life cases, taking into account all the relevant evidence including the credibility of whoever is making a claim. The explanatory-coherence account is more plausible both descriptively and normatively than a Bayesian account. (shrink)

In response to widespread use of automated decision-making technology, some have considered a right to explanation. In this paper I draw on insights from philosophical work on explanation to present a series of challenges to this idea, showing that the normative motivations for access to such explanations ask for something difficult, if not impossible, to extract from automated systems. I consider an alternative, outcomes-focused approach to the normative evaluation of automated decision-making, and recommend it as a way to pursue the (...) goods originally associated with explainability. (shrink)

This article describes contrasting ideas for a set of topics in epidemiological thinking. The premise underlying this contribution to the special edition is that researchers develop their epidemiological thinking over time through interactions with other researchers who have a variety of in-practice commitments, such as to kinds of cases and methods of analysis, and not simply to a philosophical framework for explanation. I encourage discussants from philosophy and epidemiology to draw purposefully from across a range of topics and contrasting positions, (...) and thereby pursue critical thinking in the sense of understanding ideas and practices better when we examine them in relation to alternatives. After an initial topic concerning practices for developing epidemiological literacy, a number of conceptual steps follow—the characterization of the very phenomena we might be concerned with, the scope and challenges of the field of epidemiology, the formulation of categories—before linking associations, predictions, causes and interventions and examining the confounding of purported links. Building on that conceptual basis, the remaining topics consist of issues or angles of analysis related to the complexities of inequalities within and between populations, context, and changes over the life course. The organization of topics derives from a graduate course that I teach that aims for epidemiological literacy, not technical ability in statistical formulas and data analysis, and shares the underlying premise and critical thinking goals of this article. During the topic-by-topic description, some assertions about explanation and intervention emerge, notably, that epidemiological–philosophical discussion about causality often leaves unclear or unexamined whether a modifiable factor shown to have been associated with a difference in the data from past observations should be thought of as factor that, when modified, would generate that difference going forward. The article concludes with a “Limitations of this Study” section that teases out different kinds of description–prescription relationship that are implied in undertaking philosophy of epidemiology and identifies some other considerations that are implied but not emphasized by this article. (shrink)

Kaplan and Craver :601–627, 2011) and Piccinini and Craver :283–311, 2011) argue that only mechanistic explanations of cognition are genuine causal explanations, because only evidence of mechanisms reveals the causal structure of cognition. I first argue that this claim is grounded in a commitment to the mechanistic account of causality, which cannot be endorsed by a defender of causal-nonmechanistic explanations. Then, I defend the epistemic theory of causality, which holds that causal explanations are not genuine to the extent that they (...) reveal mechanistic causal structure, but, rather, to the extent that they have evidential support and yield successful prediction, explanation, and control inferences. Finally, I enact an epistemic unification of causal explanation in cognitive science, according to which both mechanistic and nonmechanistic explanations of cognition can be genuine causal explanations. (shrink)

The paper describes an algorithm for semantic representation of behavioral contexts relative to a dichotomic decision alternative. The contexts are represented as quantum qubit states in two-dimensional Hilbert space visualized as points on the Bloch sphere. The azimuthal coordinate of this sphere functions as a one-dimensional semantic space in which the contexts are accommodated according to their subjective relevance to the considered uncertainty. The contexts are processed in triples defined by knowledge of a subject about a binary situational factor. The (...) obtained triads of context representations function as stable cognitive structure at the same time allowing a subject to model probabilistically-variative behavior. The developed algorithm illustrates an approach for quantitative subjectively-semantic modeling of behavior based on conceptual and mathematical apparatus of quantum theory. (shrink)

This article proposes a unified framework for understanding creative problem solving, namely, the explicit–implicit interaction theory. This new theory of creative problem solving constitutes an attempt at providing a more unified explanation of relevant phenomena (in part by reinterpreting/integrating various fragmentary existing theories of incubation and insight). The explicit–implicit interaction theory relies mainly on 5 basic principles, namely, (a) the coexistence of and the difference between explicit and implicit knowledge, (b) the simultaneous involvement of implicit and explicit processes in most (...) tasks, (c) the redundant representation of explicit and implicit knowledge, (d) the integration of the results of explicit and implicit processing, and (e) the iterative (and possibly bidirectional) processing. A computational implementation of the theory is developed based on the CLARION cognitive architecture and applied to the simulation of relevant human data. This work represents an initial step in the development of process-based theories of creativity encompassing incubation, insight, and various other related.. (shrink)

Why do we have records of the past and not the future? Entropic explanations for this ‘record asymmetry’ have been popular ever since Boltzmann. Foremost amongst these is Albert and Loewer’s account, which explains the record asymmetry using a low-entropy initial macrostate plus an initial probability distribution. However, the details of how this initial state underpins the record asymmetry are not fully specified. In this paper I attempt to plug this explanatory gap in two steps. First, I suggest the record (...) asymmetry is more immediately explained by the ‘fork asymmetry’, which their picture omits. Second, by relating the fork asymmetry to an initial state that’s metaphysically similar to theirs, I clarify how this ultimately underpins the record asymmetry. (shrink)

Kerry et al. criticize our discussion of causal knowledge in evidence-based medicine (EBM) and our assessment of the relevance of their dispositionalist ontology for EBM. Three issues need to be addressed in response: (1) problems concerning transfer of causal knowledge across heterogeneous contexts; (2) how predictions about the effects of individual treatments based on population-level evidence from RCTs are fallible; and (3) the relevance of ontological theories like dispositionalism for EBM.

This article examines the relationship between the precautionary principle and the well-known Hill criteria of causation. Some have charged that the Hill criteria are anti-precautionary because the...

Causal queries about singular cases, which inquire whether specific events were causally connected, are prevalent in daily life and important in professional disciplines such as the law, medicine, or engineering. Because causal links cannot be directly observed, singular causation judgments require an assessment of whether a co‐occurrence of two events c and e was causal or simply coincidental. How can this decision be made? Building on previous work by Cheng and Novick (2005) and Stephan and Waldmann (2018), we propose a (...) computational model that combines information about the causal strengths of the potential causes with information about their temporal relations to derive answers to singular causation queries. The relative causal strengths of the potential cause factors are relevant because weak causes are more likely to fail to generate effects than strong causes. But even a strong cause factor does not necessarily need to be causal in a singular case because it could have been preempted by an alternative cause. We here show how information about causal strength and about two different temporal parameters, the potential causes' onset times and their causal latencies, can be formalized and integrated into a computational account of singular causation. Four experiments are presented in which we tested the validity of the model. The results showed that people integrate the different types of information as predicted by the new model. (shrink)

Several authors have claimed that mechanisms play a vital role in distinguishing between causation and mere correlation in the social sciences. Such claims are sometimes interpreted to mean that without mechanisms, causal inference in social science is impossible. The author agrees with critics of this proposition but explains how the account of how mechanisms aid causal inference can be interpreted in a way that does not depend on it. Nevertheless, he shows that this more charitable version of the account is (...) still unsuccessful as it stands. Consequently, he advances a proposal for shoring up the account, which is founded on the possibility of acquiring knowledge of social mechanisms by linking together norms or practices found in a society. Key Words: causality • social mechanisms • interpretation • anthropology. (shrink)

Robustness arguments hold that hypotheses are more likely to be true when they are confirmed by diverse kinds of evidence. Robustness arguments require the confirming evidence to be independent. We identify two kinds of independence appealed to in robustness arguments: ontic independence —when the multiple lines of evidence depend on different materials, assumptions, or theories—and probabilistic independence. Many assume that OI is sufficient for a robustness argument to be warranted. However, we argue that, as typically construed, OI is not a (...) sufficient independence condition for warranting robustness arguments. We show that OI evidence can collectively confirm a hypothesis to a lower degree than individual lines of evidence, contrary to the standard assumption undergirding usual robustness arguments. We employ Bayesian networks to represent the ideal empirical scenario for a robustness argument and a variety of ways in which empirical scenarios can fall short of this ideal. (shrink)

The authors challenge the reigning “causal power framework” as an explanation for whether a particular outcome was actually caused by a specific potential cause. They test a new measure of causal attribution in two experiments by embedding the measure within the Structure Induction model of Singular Causation (SISC, Stephan & Waldmann, 2016).

This article examines methodological individualism in terms of the theory that invariance under intervention is the signal feature of generalizations that serve as a basis for causal explanation. This theory supports the holist contention that macro-level generalizations can explain, but it also suggests a defense of methodological individualism on the grounds that greater range of invariance under intervention entails deeper explanation. Although this individualist position is not threatened by multiple-realizability, an argument for it based on rational choice theory is called (...) into question by experimental results concerning preference reversals. Key Words: methodological individualism • mechanisms • explanation • invariance • preference reversal. (shrink)

The causal Markov condition (CMC) plays an important role in much recent work on the problem of causal inference from statistical data. It is commonly thought that the CMC is a more problematic assumption for genuinely indeterministic systems than for deterministic ones. In this essay, I critically examine this proposition. I show how the usual motivation for the CMC—that it is true of any acyclic, deterministic causal system in which the exogenous variables are independent—can be extended to the indeterministic case. (...) In light of this result, I consider several arguments for supposing indeterminism a particularly hostile environment for the CMC, but conclude that none are persuasive. Introduction Functional models and directed graphs The causal Markov theorem The causal Markov theorem and genuine indeterminism Are the exogenous variables independent? EPR Conclusion. (shrink)

A number of authors have recently used causal models to develop a promising semantics for non-backtracking counterfactuals. Briggs shows that when this semantics is naturally extended to accommodate right-nested counterfactuals, it invalidates modus ponens, and therefore violates weak centering given the standard Lewis/stalnaker interpretation of the counterfactual in terms of nearness or similarity of worlds. In this paper, I explore the possibility of abandoning the Lewis/stalnaker interpretation for some alternative that is better suited to accommodate the causal modeling semantics. I (...) argue that a revision of McGee’s semantics can accommodate CM semantics without sacrificing weak centering, and that CM semantics can therefore be situated within a general semantics for counterfactuals that is based on the nearness or similarity of worlds. (shrink)

The faithfulness condition (FC) is a useful principle for inferring causal structure from statistical data. The usual motivation for the FC appeals to theorems showing that exceptions to it have probability zero, provided that some apparently reasonable assumptions obtain. However, some have objected that, the theorems notwithstanding, exceptions to the FC are probable in commonly occurring circumstances. I argue that exceptions to the FC are probable in the circumstances specified by this objection only given the presence of a condition that (...) I label homogeneity, and furthermore that this condition typically does not obtain in the FC’s intended domain of application. (shrink)

Meek and Glymour use the graphical approach to causal modeling to argue that one and the same norm of rational choice can be used to deliver both causal-decision-theoretic verdicts and evidential-decision-theoretic verdicts. Specifically, they argue that if an agent maximizes conditional expected utility, then the agent will follow the causal decision theorist’s advice when she represents herself as intervening, and will follow the evidential decision theorist’s advice when she represents herself as not intervening. Since Meek and Glymour take no stand (...) on whether agents should represent themselves as intervening, they provide more general advice than standard causal decision theorists and evidential decision theorists. But I argue here that even Meek and Glymour’s advice is not sufficiently general. This is because their advice is not sensitive to the distinct ways in which agents can fail to intervene, and there are decision-making contexts in which agents can reasonably have non-extreme confidence that they are intervening. I then show that the most natural extension of Meek and Glymour’s framework fails, but offer a generalization of my “Interventionist Decision Theory” that does not suffer from the same problems. (shrink)

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)

Given that an analysis of disposition ascription cannot be made in terms of a simple subjunctive conditional, we present a multiply qualified conditional analysis that places disposition ascription within an implicit fundamental causal conceptual typography within which a disposition ascription is embedded, framed, and understood. By placing the multiply qualified analysis within an implicit causal matrix involving a focal cause, pathway of influence, mechanism of action, contributing/partial cause, mediator, extrinsic moderator,, intrinsic moderator, and manifestation, we show how this analysis evades (...) counterexamples and arguments raised against the simple conditional analysis. We then indicate how the causal conceptual landscape provides a roadmap for unpacking and explicating parameters of the ceteris paribus qualifier to acquit the analysis of charges of being vacuous or irredeemably vague. (shrink)

In this paper, I use interventionist causal models to identify some novel Newcomb problems, and subsequently use these problems to refine existing interventionist treatments of causal decision theory. The new Newcomb problems that make trouble for existing interventionist treatments involve so-called ‘exotic choice’—that is, decision-making contexts where the agent has evidence about the outcome of her choice. I argue that when choice is exotic, the interventionist can adequately capture causal decision-theoretic reasoning by introducing a new interventionist approach to updating on (...) exotic evidence. But I also argue that this new updating procedure is principled only if the interventionist trades in the typical interventionist conception of choice for an alternative Ramseyan conception. I end by arguing that the guide to exotic choice developed here may, despite its name, be useful in some everyday contexts. (shrink)

The aim of this paper is to determine whether and to what extent Woodward’s interventionist theory of causation is variable relative. In an influential review, Strevens has accused Woodward’s account of a damaging form of variable relativity, according to which obviously false causal claims can be made true by choosing a depleted variable set. Following McCain, I show that Strevens’ objection doesn’t succeed. However, Woodward also wants to avoid another kind of variable relativity, according to which it can be true (...) that X is a cause of Y in one set of background conditions, but false in another. I show that Woodward’s account is problematically overpermissive, unless there are restrictions on the values that certain variables can take. I formulate a modified account that makes these restrictions explicit, then use it to argue that Woodward’s attempt to avoid relativity to background conditions is misguided. On the best interpretation of the interventionist theory, causal claims are assessed relative to a particular kind of variable set. Thus, I conclude that the theory should be understood as variable relative, in a specific, unproblematic sense. (shrink)

I apply James Woodward’s interventionist theory of causation to organic chemistry, modelling three different ways that chemists are able to manipulate the reaction conditions in order to control the outcome of a reaction. These consist in manipulations to the reaction kinetics, thermodynamics, and whether the kinetics or thermodynamics predominates. It is possible to construct interventionist causal models of all of these kinds of manipulation, and therefore to account for them using Woodward’s theory. However, I show that there is an alternate, (...) more illuminating way of thinking about the third kind of reaction control, according to which chemists are thought of as manipulating which causal system is instantiated. I show that our ability to manipulate which system is instantiated is an important part of our ability to control the world, as is therefore especially relevant to interventionism. Thus, considering examples from organic chemistry leads to the identification of an important extension to Woodward’s theory. Finally, this investigation into reaction control in organic chemistry also has a more general implication: it suggests that interventionism results in a version of pragmatism about causation. (shrink)

ABSTRACT Contrastive and deviant/default accounts of causation are becoming increasingly common. However, discussions of these accounts have neglected important questions, including how the context determines the contrasts, and what shared knowledge is necessary for this to be possible. I address these questions, using organic chemistry as a case study. Focusing on one example—nucleophilic substitution—I show that the kinds of causal claims that can be made about an organic reaction depend on how the reaction is modelled, and argue that paying attention (...) to the various ways that reactions are modelled has important implications for our understanding of causation. _1_ Introduction _2_ General Contrastive Causal Claims in Organic Chemistry _3_ Deviant Causal Claims in Organic Chemistry _4_ Nucleophilic Substitution Reactions _5_ The Causal Modelling Tradition _5.1_ The type/token distinction _6_ Competing Reactions _6.1_ Type- and token-causal claims, variables, and values of variables _7_ Disambiguation of ‘Reaction’ _8_ Reaction Kinds _9_ Specific Reactions _9.1_ Specific reactions and token-causal claims _9.2_ Specific reactions and type-causal claims _10_ Implications _10.1_ Kinds of causal claim _10.2_ Contrastive and deviant causal claims _10.3_ Model relativity. (shrink)

A uniform theory of conditionals is one which compositionally captures the behavior of both indicative and subjunctive conditionals without positing ambiguities. This paper raises new problems for the closest thing to a uniform analysis in the literature (Stalnaker, Philosophia, 5, 269–286 (1975)) and develops a new theory which solves them. I also show that this new analysis provides an improved treatment of three phenomena (the import-export equivalence, reverse Sobel-sequences and disjunctive antecedents). While these results concern central issues in the study (...) of conditionals, broader themes in the philosophy of language and formal semantics are also engaged here. This new analysis exploits a dynamic conception of meaning where the meaning of a symbol is its potential to change an agent’s mental state (or the state of a conversation) rather than being the symbol’s content (e.g. the proposition it expresses). The analysis of conditionals is also built on the idea that the contrast between subjunctive and indicative conditionals parallels a contrast between revising and consistently extending some body of information. (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)

This is one of a pair of discussion notes comparing some features of the account of causation in Wolfgang Spohn’s Laws of Belief with the “interventionist” account in James Woodward’s Making Things Happen. This note locates the core difference of the accounts in the fact that Woodward’s account follows an epistemological order, while Spohn’s follows a conceptual order. This unfolds in five further differences: type- versus token-level causation, reference to time, actual/counterfactual intervention versus epistemic/suppositional wiggling, a circular versus a circle-free (...) conception of the circumstances of a direct causal relation, and absolute versus model-relative causation. (shrink)

The paper will show how one may rationalize one-boxing in Newcomb's problem and drinking the toxin in the Toxin puzzle within the confines of causal decision theory by ascending to so-called reflexive decision models which reflect how actions are caused by decision situations (beliefs, desires, and intentions) represented by ordinary unreflexive decision models.