I show that locality, as it occurs in EPR arguments for the incompleteness of quantum mechanics, can be construed as causal independence understood in terms of Lewis' counterfactual analysis of causation. This construal has two benefits. It supplements recent analyses, which have not treated locality in detail. And it clarifies the relation between two EPR arguments that have recently been distinguished. It shows that the simpler of the two is more complex than has been thought; and that the (...) other argument does not need 'counterfactual definiteness'. (shrink)

Causal independence of the simultaneous positions and momenta of two distinguishable particles in nonrelativistic physics and causal independence of events in two relatively spacelike regions of space-time in relativity are analyzed and discussed. This review paper formulates causal independence in a general and operational way and summarizes the inferences drawn from it in non-relativistic quantum mechanics, classical relativistic point mechanics, quantum field theory, and classical field theory. Special attention is given to the open question (...) of the relationship between local independence and commutativity in quantum field theory. (shrink)

The aim of the paper is to explicate the concept of causal independence between sets of factors and Reichenbach's screening-off-relation in probabilistic terms along the lines of Suppes' probabilistic theory of causality (1970). The probabilistic concept central to this task is that of conditional stochastic independence. The adequacy of the explication is supported by proving some theorems about the explicata which correspond to our intuitions about the explicanda.

David Lewis’s semantics for counterfactuals remains the standard view. Yet counter-examples have emerged, which suggest a need to invoke causal independence, and thus threaten conceptual circularity. I will review some of these counter-examples (§§1–2), illustrate how causal independence proves useful (§3), and suggest that any resulting circularity is unproblematic (§4).

In his well-known 1952 dialogue Max Black describes a counterexample to the Principle of the Identity of Indiscernibles (PII). The counterexample is a world containing nothing but two purportedly indiscernible iron spheres. Reflecting on Black's example, Robert Adams uses the possibility of a world containing two almost indiscernible spheres to argue for the possibility of the indiscernible spheres world. One of Adams's almost indiscernible spheres has a small impurity, and, Adams writes, "Surely... the absence of the impurity would not make (...) such a universe impossible." The appeal to "surely" constitutes a gap in Adams's argument. This paper bridges the gap with a premise that exploits the counterfactual conditional and the related notion of causal independence. The paper then argues that causal independence bears in a similar way on an issue Nathan Salmon raises concerning Kripke's argument for the Essentiality of Origins. (shrink)

Ekstein has shown that causal independence neither implies nor is implied by commutativity in an infinite-dimensional, reducible construction. DeFacio and Taylor have presented a finite-dimensional irreducible example of Ekstein's proposition. Avishai and Ekstein have shown that the original question regarding locality for algebraic quantum field theories remainsopen. We concur with that claim and offer additional arguments. A new denumerably infinite-dimensional, irreducible example is presented here which shows that a sort of “orthogonality” among operators is involved. Some observations on (...) localC*-andW*-algebras are given. (shrink)

In Kvart (1991a), I discussed the analysis of causal relevance presented in A Theory of Counterfactuals (1986) (and first in 1975). I explained there in what respect the notion captured by the analysis of Kvart (1986) is a mere approximation to the requisite notion of causal relevance. In this paper I present another analysis of causal relevance, devoid of the shortcoming of its predecessor. The present analysis of causal relevance is, again, grounded in a chancelike notion (...) of objective probability. The correlative notion of causal independence is analyzed as holding in case there is a so-called causal impact series, which is defined here. The basic notions used for this analysis are the notion of a differentiator, which upsets an equiprobability case, and that of a blocker, which restores it. (shrink)

Academic freedom has often been defended in a progressivist manner: without academic freedom, creativity would be in peril, and with it the advancement of knowledge, i.e. the epistemic progress in science. In this paper, I want to critically discuss the limits of such a progressivist defense of academic freedom, also known under the label ‘argument from truth.’ The critique is offered, however, with a constructive goal in mind, namely to offer an alternative account that connects creativity and academic freedom in (...) a way that goes beyond mere reference to epistemic progress and involves reference to the freedom to think independently as the freedom we mean when we point to creativity and when we point to academic freedom. The resulting causal independence account is not only epistemologically stronger than a progressivist account, it also allows to counter the curbing of academic freedom in the name of progress. The latter becomes key, for instance, when authoritarian political regimes limit or negate academic freedom with reference to an epistemic progress suitably defined for that regime. (shrink)

Some have argued that no analysis of counterfactual conditionals can succeed without appealing to causal notions. Such authors claim that, in determining what would transpire had some events gone differently, we hold fixed everything that is causally independent from those events. Call this view Causal Independence. Some have argued that we need Causal Independence to accommodate intuitive judgments about certain kinds of counterfactuals in indeterministic worlds. The aim of this paper is to show that, contra (...) these authors, Causal Independence systematically delivers counterintuitive results for a certain subset of such counterfactuals-namely, those involving causation by double prevention. I conclude that intuitions about such counterfactuals do not motivate Causal Independence, at least in any form in which it has thus far been articulated. However, I suggest that a refined Causal Independence thesis that presupposes a kind of causal pluralism might be able to accommodate these intuitions, though such a refined version of Causal Independence may not conflict with reductive analyses of causal notions that appeal to counterfactuals after all. (shrink)

Independence of Conditionals (IC) has recently been proposed as a basic rule for causal structure learning. If a Bayesian network represents the causal structure, its Conditional Probability Distributions (CPDs) should be algorithmically independent. In this paper we compare IC with causal faithfulness (FF), stating that only those conditional independences that are implied by the causal Markov condition hold true. The latter is a basic postulate in common approaches to causal structure learning. The common spirit (...) of FF and IC is to reject causal graphs for which the joint distribution looks ‘non-generic’. The difference lies in the notion of genericity: FF sometimes rejects models just because one of the CPDs is simple, for instance if the CPD describes a deterministic relation. IC does not behave in this undesirable way. It only rejects a model when there is a non-generic relation between different CPDs although each CPD looks generic when considered separately. Moreover, it detects relations between CPDs that cannot be captured by conditional independences. IC therefore helps in distinguishing causal graphs that induce the same conditional independences (i.e., they belong to the same Markov equivalence class). The usual justification for FF implicitly assumes a prior that is a probability density on the parameter space. IC can be justified by Solomonoff’s universal prior, assigning non-zero probability to those points in parameter space that have a finite description. In this way, it favours simple CPDs, and therefore respects Occam’s razor. Since Kolmogorov complexity is uncomputable, IC is not directly applicable in practice. We argue that it is nevertheless helpful, since it has already served as inspiration and justification for novel causal inference algorithms. (shrink)

This essay explains what the Causal Markov Condition says and defends the condition from the many criticisms that have been launched against it. Although we are skeptical about some of the applications of the Causal Markov Condition, we argue that it is implicit in the view that causes can be used to manipulate their effects and that it cannot be surrendered without surrendering this view of causation.

This article defends the notion of downward causation, relating it to a notion of conditional independence.

Mathias Frisch has argued that the requirement that electromagnetic dispersion processes are causal adds empirical content not found in electrodynamic theory. I urge that this attempt to reconstitute a local principle of causality in physics fails. An independent principle is not needed to recover the results of dispersion theory. The use of ‘causality conditions’ proves to be the mere adding of causal labels to an already presumed fact. If instead one seeks a broader, independently formulated grounding for the (...) conditions, that grounding either fails or dissolves into vagueness and ambiguity, as has traditionally been the fate of candidate principles of causality. Introduction Scattering in Classical Electrodynamics Sufficiency of the Physics Failure of the Principle of Causality Proposed 4.1 A sometimes principle 4.2 The conditions of applicability are obscure 4.3 Effects can come before their causes 4.4 Vagueness of the relata and of the notion of causal process Conclusion CiteULike Connotea Del.icio.us What's this? (shrink)

Assumptions of stochastic independence are crucial to statistical models in science. Under what circumstances is it reasonable to suppose that two events are independent? When they are not causally or logically connected, so the standard story goes. But scientific models frequently treat causally dependent events as stochastically independent, raising the question whether there are kinds of causal connection that do not undermine stochastic independence. This paper provides one piece of an answer to this question, treating the simple (...) case of two tossed coins with and without a midair collision. (shrink)

Democratic decision-making is often defended on grounds of the ‘wisdom of crowds’: decisions are more likely to be correct if they are based on many independent opinions, so a typical argument in social epistemology. But what does it mean to have independent opinions? Opinions can be probabilistically dependent even if individuals form their opinion in causal isolation from each other. We distinguish four probabilistic notions of opinion independence. Which of them holds depends on how individuals are causally affected (...) by environmental factors such as commonly perceived evidence. In a general theorem, we identify causal conditions guaranteeing each kind of opinion independence. These results have implications for whether and how ‘wisdom of crowds’ arguments are possible, and how truth-conducive institutions can be designed. (shrink)

Perfect adaptation in a dynamical system is the phenomenon that one or more variables have an initial transient response to a persistent change in an external stimulus but revert to their original value as the system converges to equilibrium. With the help of the causal ordering algorithm, one can construct graphical representations of dynamical systems that represent the causal relations between the variables and the conditional independences in the equilibrium distribution. We apply these tools to formulate sufficient graphical (...) conditions for identifying perfect adaptation from a set of first-order differential equations. Furthermore, we give sufficient conditions to test for the presence of perfect adaptation in experimental equilibrium data. We apply this method to a simple model for a protein signalling pathway and test its predictions in both simulations and using real-world protein expression data. We demonstrate that perfect adaptation can lead to misleading orientation of edges in the output of causal discovery algorithms. (shrink)

Leibniz thinks that every created substance is causally active, and yet causally independent of every other: none can cause changes in any but itself. This is not controversial. But Leibniz also thinks that every created substance is existentially independent of every other: it is metaphysically possible for any to exist with or without any other. This is controversial. I argue that, given a mainstream reading of Leibniz’s essentialism, if one accepts the former, uncontroversial interpretation concerning causal independence, then (...) one ought also to accept the latter, controversial one concerning existential independence. This is a new way to defend the ‘existential independence’ interpretation. Moreover, this defense provides a new approach for defending the broadly ‘non-logical’ interpretive camp in the longstanding debate over Leibniz’s views on incompossibility, against perhaps the strongest objection leveled by advocates of the opposing broadly ‘logical’ interpretation. (shrink)

According to Hugh Mellor in Real Time II (1998, Ch. 12), assuming the logical independence of causal facts and the 'law of large numbers', causal loops are impossible because if they were possible they would produce inconsistent sets of frequencies. I clarify the argument, and argue that it would be preferable to abandon the relevant independence assumption in the case of causal loops.

It is widely held that, as Morgenbesser’s case is usually taken to show, considerations of causal or probabilistic dependence should enter into the evaluation of counterfactuals. This paper challenges that idea. I present a modified version of Morgenbesser’s case and show how probabilistic approaches to counterfactuals are in serious trouble. Specifically, I show how probabilistic approaches run into a dilemma in characterizing probabilistic independence. The modified case also illustrates a difficulty in defining causal independence. I close (...) with a suggestion for a strategy to handle this difficulty. (shrink)

Recent work on the interpretation of counterfactual conditionals has paid much attention to the role of causal independencies. One influential idea from the theory of Causal Bayesian Networks is that counterfactual assumptions are made by intervention on variables, leaving all of their causal non-descendants unaffected. But intervention is not applicable across the board. For instance, backtracking counterfactuals, which involve reasoning from effects to causes, cannot proceed by intervention in the strict sense, for otherwise they would be equivalent (...) to their consequents. We discuss these and similar cases, focusing on two factors which play a role in determining whether and which causal parents of the manipulated variable are affected: Speakers' need for an explanation of the hypothesized state of affairs, and differences in the ‘resilience’ of beliefs that are independent of degrees of certainty. We describe the relevant theoretical notions in some detail and provide experimental evidence that these factors do indeed affect speakers' interpretation of counterfactuals. (shrink)

Causal Bayes nets have been developed in philosophy, statistics, and computer sciences to provide a formalism to represent causal structures, to induce causal structure from data and to derive predictions. Causal Bayes nets have been used as psychological theories in at least two ways. They were used as rational, computational models of causal reasoning and they were used as formal models of mental causal models. A crucial assumption made by them is the Markov condition, (...) which informally states that variables are independent of other variables that are not their direct or indirect effects conditional on their immediate causes. Whether people’s inferences conform to the causal Markov and the faithfulness condition has recently been investigated empirically. A review of respective research indicates that inferences frequently violate these conditions. This finding challenges some uses of causal Bayes nets in psychology. They entail that causal Bayes nets may not be appropriate to derive predictions for causal model theories of causal reasoning. They also question whether causal Bayes nets as a rational model are empirically descriptive. They do not challenge, however, causal Bayes nets as normative models and their usage as formal models of causal reasoning. (shrink)

The problem facing us in this paper is that of how to analyze the notion of causal relevance. This is the inverse relation of causal dependence: A is causally irrelevant to C iff C is causally independent of A. As an example of causal relevance, consider: Example 1: A - The American astronaut on Mir scratched his left ear exactly an hour ago B - I am writing this paper right now. Intuitively, A was not causally relevant (...) to B. It is this kind of intuition that I’ll mostly be relying on when analyzing the notion of causal relevance. (shrink)

Recent work on the interpretation of counterfactual conditionals has paid much attention to the role of causal independencies. One influential idea from the theory of Causal Bayesian Networks is that counterfactual assumptions are made by intervention on variables, leaving all of their causal non‐descendants unaffected. But intervention is not applicable across the board. For instance, backtracking counterfactuals, which involve reasoning from effects to causes, cannot proceed by intervention in the strict sense, for otherwise they would be equivalent (...) to their consequents. We discuss these and similar cases, focusing on two factors which play a role in determining whether and which causal parents of the manipulated variable are affected: Speakers' need for an explanation of the hypothesized state of affairs, and differences in the ‘resilience’ of beliefs that are independent of degrees of certainty. We describe the relevant theoretical notions in some detail and provide experimental evidence that these factors do indeed affect speakers' interpretation of counterfactuals. (shrink)

If the laws are deterministic, then standard theories of counterfactuals are forced to reject at least one of the following conditionals: 1) had you chosen differently, there would not have been a violation of the laws of nature; and 2) had you chosen differently, the initial conditions of the universe would not have been different. On the relevant readings—where we hold fixed factors causally independent of your choice—both of these conditionals appear true. And rejecting either one leads to trouble for (...) philosophical theories which rely upon counterfactual conditionals—like, for instance, causal decision theory. Here, I outline a semantics for counterfactual conditionals which allows us to accept both (1) and (2). And I discuss how this semantics deals with objections to causal decision theory from Arif Ahmed. (shrink)

The principle of 'information causality' can be used to derive an upper bound---known as the 'Tsirelson bound'---on the strength of quantum mechanical correlations, and has been conjectured to be a foundational principle of nature. In this paper, however, I argue that the principle has not to date been sufficiently motivated to play this role; the motivations that have so far been given are either unsatisfactorily vague or else amount to little more than an appeal to intuition. I then consider how (...) one might begin to successfully motivate the principle. I argue that a compelling way of so doing is to understand it as a generalisation of Einstein's principle of the mutually independent existence---the 'being-thus'---of spatially distant things, interpreted as a special methodological principle. More specifically: I describe an argument, due to Demopoulos, to the effect that the quantum-mechanical no-signalling condition can be viewed as a generalisation, appropriate to an irreducibly statistical theory such as quantum mechanics, of the Einsteinian principle. And I then argue that a compelling way to motivate information causality is to in turn consider it as a further generalisation of the Einsteinian principle that is appropriate to a theory of communication. I nevertheless describe important obstacles that must yet be overcome if the project of establishing information causality as a foundational principle of nature is to succeed. (shrink)

In this dissertation, I develop and defend a model of causation that allows for dualist mental causation in worlds where the physical domain is physically complete. In Part I, I present the dualist ontology that will be assumed throughout the thesis and identify two challenges for models of mental causation within such an ontology: the exclusion worry and the common cause worry. I also argue that a proper response to these challenges requires a thoroughly lightweight account of causation, i.e. an (...) account that allows for causes to be metaphysically distinct from the phenomena that produce or physically necessitate their effects. In Part II, I critically evaluate contemporary responses to these challenges from the philosophical literature. In particular, I discuss List and Stoljar’s criticism of exclusion worries, Kroedel’s alternative dualist ontology, concerns about the notion of causal sufficiency, and Lowe’s models of dualist mental causation. I argue that none of these proposals provide independent motivation for a thoroughly lightweight account of causation and therefore leave room for improvement. In the first four chapters of Part III, I develop a thoroughly lightweight model of causation, which builds on interventionist approaches to causation. First, I explain how so-called ‘holding fixed’-requirements in standard interventionist accounts stand in the way of dualist mental causation. I then argue that interventionist accounts should impose a robustness condition on causal correlations and that, with this condition in place, the ‘holding fixed’-requirements can be weakened such that they do allow for dualist mental causation. I dub the interventionist model with such weakened ‘holding fixed’-requirements ‘insensitive interventionism’, argue that it can counter the exclusion worry as well as the common cause worry, and explain under which circumstances it would predict there to be dualist mental causation. Importantly, these circumstances might, for all we know, hold in the actual world. In the final three chapters of Part III, I defend insensitive interventionism against some objections. I consider the objection that causation must be productive, the objection that causes must physically necessitate their effects, and the objection that insensitive interventionism is too permissive. I respond by drawing from the literature on causation by absences and on the relation between causation and fundamental physics. Overall, insensitive interventionism performs as well as standard interventionist accounts. I conclude that insensitive interventionism is a credible model of causation. (shrink)

With the advent of evolutionary developmental research, or EvoDevo, there is hope of discovering the roles that the genetic bases of development play in morphological evolution. Studies in EvoDevo span several levels of organismal organization. Low-level studies identify the ultimate genetic changes responsible for morphological variation and diversity. High-level studies of development focus on how genetic differences affect the dynamics of gene networks and epigenetic interactions to modify morphology. Whereas an increasing number of studies link independent acquisition of homoplastic or (...) convergent morphologies to similar changes in the genomes, homoplasies are not always found to have identical low-level genetic underpinnings. This suggests that a combination of low- and high-level approaches may be useful in understanding the relationship between genetic and morphological variation. Therefore, as an empirical and conceptual framework, we propose the causality horizon to signify the lowest level that allows linking homoplastic morphologies to similar changes in the development. A change in a system below the causality horizon cannot be generalized. In more concrete terms, homoplastic morphologies cannot be reduced to the same change in gene regulation when that change occurs below the causality horizon; rather, a higher-level mechanism should be identified. (shrink)

Earlier version on philsci-archive.pitt.edu; latest version.

In this paper, I want to substantiate three related claims regarding causal discovery from non-experimental data. Firstly, in scientific practice, the problem of ignorance is ubiquitous, persistent, and far-reaching. Intuitively, the problem of ignorance bears upon the following situation. A set of random variables V is studied but only partly tested for (conditional) independencies; i.e. for some variables A and B it is not known whether they are (conditionally) independent. Secondly, Judea Pearl’s most meritorious and influential algorithm for (...) class='Hi'>causal discovery (the IC algorithm) cannot be applied in cases of ignorance. It presupposes that a full list of (conditional) independence relations is on hand and it would lead to unsatisfactory results when applied to partial lists. Finally, the problem of ignorance is successfully treated by means of ALIC, the adaptive logic for causal discovery presented in this paper. (shrink)

This paper expounds that besides the well-known spatio-temporal problem there is a causal problem of entanglement: even when one neglects spatio-temporal constraints, the peculiar statistics of EPR/B experiment is inconsistent with usual principles of causal explanation as stated by the theory of causal Bayes nets. The conflict amounts to a dilemma that either there are uncaused correlations or there are caused independences . I argue that the central ideas of causal explanations can be saved if one (...) accepts the latter horn and explains the unfaithful independences by a stable fine-tuning of the causal parameters. (shrink)

I propose an argument for the thesis that laws of nature are necessary in the sense of holding in all worlds sharing the properties of the actual world, on the basis of a principle I propose to call the Causal Criterion of Reality . The CCR says: for an entity to be real it is necessary and sufficient that it is capable to make a difference to causal interactions. The crucial idea here is that the capacity to interact (...) causally - or to contribute to determining causal interactions - is not only the ultimate metaphysical ground for the existence of an entity, but it also provides a criterion for determining the nature of that entity, i.e. its properties. The alternative is to conceive of laws of nature as contingent: they could be different from what they are like in the actual world, where that possibility is understood to be metaphysical, not only epistemic. For the sake of this paper, I shall accept Armstrong's thesis that laws of nature are relations between universals. I also follow Armstrong in the view that both the existence and the properties of particulars are metaphysically independent of the existence and identity of other particular. However, what is controversial and what I shall challenge is his thesis that universals are like particulars in the following respect: according to Armstrong, each universal is a logically distinct entity whose existence and identity is independent of the existence and identity of other universals. My aim in this paper is to show that the identity of a universal is entirely determined by its lawful relations to other universals. The crucial premise I use is the thesis that the CCR is a universal criterion, which applies both to particulars and universals. From the thesis that the identity of a universal is exclusively determined by laws, it follows that laws are necessary in the sense that they cannot differ without the universals they link also being different. This creates a difficulty for those authors who, as Armstrong, accept the CCR but nevertheless defend the view that laws are contingent. (shrink)

Causal exclusion arguments are taken to threaten the autonomy of the special sciences, and the causal efficacy of mental properties. A recent line of response to these arguments has appealed to “independently plausible” and “well grounded” theories of causation to rebut key premises. In this paper I consider two papers which proceed in this vein and show that they share a common feature: they both require causes to be proportional to their effects. I argue that this feature is (...) a bug, and one that generalises: any attempt to rescue the autonomy of the special sciences, or the efficacy of the mental, from exclusion worries had better not look to proportionality for help. (shrink)

We start this paper by arguing that causality should, in analogy with force in Newtonian physics, be understood as a theoretical concept that is not explicated by a single definition, but by the axioms of a theory. Such an understanding of causality implicitly underlies the well-known theory of causal nets and has been explicitly promoted by Glymour. In this paper we investigate the explanatory warrant and empirical content of TCN. We sketch how the assumption of directed cause–effect relations can (...) be philosophically justified by an inference to the best explanation. We then ask whether the explanations provided by TCN are merely post-facto or have independently testable empirical content. To answer this question we develop a fine-grained axiomatization of TCN, including a distinction of different kinds of faithfulness. A number of theorems show that although the core axioms of TCN are empirically empty, extended versions of TCN have successively increasing empirical content. (shrink)

Jaegwon Kim argues that unreduced mental causes are excluded from efficacy because physical causes are sufficient in themselves. One response to this causal exclusion argument is to embrace some form of overdetermination. In this paper I consider two forms of overdetermination. Independent overdetermination suggests that two individually sufficient causes bring about one effect. This model fails because the sufficiency of one cause renders the other cause unnecessary. Dependent overdetermination suggests that a physical cause is necessary and sufficient for a (...) given effect, but it necessitates a mental cause of the effect as well. This model fails because the necessity of the mental cause renders the physical cause individually insufficient. (shrink)

The epistemic theory of causality views causality as a tool that helps us to predict, explain and control our world, rather than as a relation that exists independently of our epistemic practices. In this chapter, we first provide an introduction to the epistemic theory of causality. We then outline four considerations that motivate the epistemic theory: the failure of standard theories of causality; parsimony; the epistemology of causality; and neutrality. We illustrate these four considerations in the contexts of the social (...) sciences and the cognitive sciences. We argue that the epistemic theory provides a very natural account of causality across these contexts. (shrink)

We used a new method to assess how people can infer unobserved causal structure from patterns of observed events. Participants were taught to draw causal graphs, and then shown a pattern of associations and interventions on a novel causal system. Given minimal training and no feedback, participants in Experiment 1 used causal graph notation to spontaneously draw structures containing one observed cause, one unobserved common cause, and two unobserved independent causes, depending on the pattern of associations (...) and interventions they saw. We replicated these findings with less‐informative training (Experiments 2 and 3) and a new apparatus (Experiment 3) to show that the pattern of data leads to hidden causal inferences across a range of prior constraints on causal knowledge. (shrink)

I argue that one central aspect of the epistemology of causation, the use of causes as evidence for their effects, is largely independent of the metaphysics of causation. In particular, I use the formalism of Bayesian causal graphs to factor the incremental evidential impact of a cause for its effect into a direct cause-to-effect component and a backtracking component. While the “backtracking” evidence that causes provide about earlier events often obscures things, once we our restrict attention to the cause-to-effect (...) component it is true to say promoting (inhibiting) causes raise (lower) the probabilities of their effects. This factoring assumes the same form whether causation is given an interventionist, counterfactual or probabilistic interpretation. Whether we think about causation in terms of interventions and causal graphs, counterfactuals and imaging functions, or probability raising against the background of causally homogenous partitions, if we describe the essential features of a situation correctly then the incremental evidence that a cause provides for its effect in virtue of being its cause will be the same. (shrink)

The physical realm is causally closed, according to physicalists like me. But why is it causally closed, what metaphysically explains causal closure? I argue that reductive physicalists are committed to one explanation of causal closure to the exclusion of any independent explanation, and that as a result, they must give up on using a causal argument to attack mind–body dualism. Reductive physicalists should view dualism in much the way that we view the hypothesis that unicorns exist, or (...) that the Kansas City Royals won the 2003 World Series: false, but not objectionable in any distinctively causal way. My argument turns on connections between explanation, counterfactuals, and inductive confirmation. (shrink)

This article considers the extent to which Bayesian networks with imprecise probabilities, which are used in statistics and computer science for predictive purposes, can be used to represent causal structure. It is argued that the adequacy conditions for causal representation in the precise context—the Causal Markov Condition and Minimality—do not readily translate into the imprecise context. Crucial to this argument is the fact that the independence relation between random variables can be understood in several different ways (...) when the joint probability distribution over those variables is imprecise, none of which provides a compelling basis for the causal interpretation of imprecise Bayes nets. I conclude that there are serious limits to the use of imprecise Bayesian networks to represent causal structure. (shrink)

This book, by one of the pre-eminent philosophers of science writing today, offers the most comprehensive account available of causal asymmetries. Causation is asymmetrical in many different ways. Causes precede effects; explanations cite causes not effects. Agents use causes to manipulate their effects; they don't use effects to manipulate their causes. Effects of a common cause are correlated; causes of a common effect are not. This book explains why a relationship that is asymmetrical in one of these regards is (...) asymmetrical in the others. Hausman discovers surprising hidden connections between theories of causation and traces them all to an asymmetry of independence. This is a major book for philosophers of science that will also prove insightful to economists and statisticians. (shrink)

In this paper I present a limitation to what may be called strictly-interventionistic causal-model semantic theories for subjunctive conditionals. And I offer a line of response to Briggs’ counterexample to Modus Ponens—given within a strictly-interventionistic framework—for the subjunctive conditional. The paper also contains some discussion of backtracking counterfactuals and backtracking interpretations. The limitation inherent to strict interventionism is brought out via a class of counterexamples. A causal-model semantics is strictly interventionistic just in case the procedure it gives for (...) evaluating a subjunctive conditional requires making the values of the variables implicated in the antecedent independent from the values of the parents of these antecedent variables. Most causal-model semantic theories that have gained attention are strictly interventionistic. (shrink)

The notion of common cause closedness of a classical, Kolmogorovian probability space with respect to a causal independence relation between the random events is defined, and propositions are presented that characterize common cause closedness for specific probability spaces. It is proved in particular that no probability space with a finite number of random events can contain common causes of all the correlations it predicts; however, it is demonstrated that probability spaces even with a finite number of random events (...) can be common cause closed with respect to a causal independence relation that is stronger than logical independence. Furthermore it is shown that infinite, atomless probability spaces are always common cause closed in the strongest possible sense. Open problems concerning common cause closedness are formulated and the results are interpreted from the perspective of Reichenbach's Common Cause Principle. (shrink)

Democratic decision-making is often defended on grounds of the ‘wisdom of crowds’: decisions are more likely to be correct if they are based on many independent opinions, so a typical argument in social epistemology. But what does it mean to have independent opinions? Opinions can be probabilistically dependent (threatening the ‘wisdom of crowds’) even if individuals form their opinion in causal isolation from each other. We distinguish four probabilistic notions of opinion independence. Which of them holds depends on (...) how individuals are causally affected by environmental factors such as commonly perceived evidence. In a general theorem, we identify causal conditions guaranteeing each kind of opinion independence. These results have implications for whether and how ‘wisdom of crowds’ arguments are possible, and how truth-conducive institutions can be designed. (shrink)

The chapter explores whether, or to what extent, recent work in experimental philosophy puts pressure on the idea that the concept of causation is ‘egalitarian’. Causal selection – where experimental subjects tend to rate the causal strength of (for example) a norm-violator more strongly than a non-norm-violator – is a well established phenomenon, and is in prima facie tension with an egalitarian conception of causation; it also, indirectly, puts prima facie pressure on the idea that causation is a (...) worldly phenomenon whose obtaining is independent of facts about norms. The chapter explores both the various psychological mechanisms and the broadly pragmatic approaches to explaining causal selection. It argues that the answer to the question whether or not the concept of causation is egalitarian is currently significantly empirically underdetermined, and suggests some avenues for further investigation. (shrink)

In "Causes and "If P, Even If X, still Q," Philosophy 57 (July 1982), Ted Honderich cites my "The Direction of Causation and the Direction of Conditionship," journal of Philosophy 73 (April 22, 1976) as an example of an account of causal priority that lacks the proper character. After emending Honderich's description of the proper character, I argue that my attempt to account for one-way causation in terms of one-way causal conditionship does not totally lack it. Rather than (...) emphasize the singularity of an effect, as Honderich does, I emphasize the multiplicity of independent factors in a causal circumstance. (shrink)