The principle of causal exclusion is based on two distinct causal notions: causal sufficiency and causation simpliciter. The principle suggests that the former has the power to exclude the latter. But that is problematic since it would amount to claiming that sufficient causes alone can take the roles of causes simpliciter. Moreover, the principle also assumes that events can sometimes have both sufficient causes and causes simpliciter. This assumption is in conflict with the first (...) part of the principle that claims that sufficient causes must exclude causes simpliciter. (shrink)

Given the importance of the principle of causality for the demonstration of God’s existence, this paper attempts to justify the evidence and necessity of the principle of causality, by following Fr. Fabro’s thomistic defense—based on the notion of participation—but adding a particular emphasis on the notion of “being which is not per se,” this latter as an explanatory notion of the notion of “being which is by participation.” The introductory remarks touch upon two misunderstandings regarding the notion of (...) participation employed as a subject of the principle of causality: the first, considering “participated being” as a synonim of “received being,” and the second, the confusion, regarding the notion of being by participation, between its notional dependence on the notion of being as a whole and its real dependence on the real intensive Being. Then, in the first point, the defense of the principle of causality is distinguished from other related problems, like the particular applications of this principle. In the second and main point, we try to justify the necessity of being caused in the being which is experienced as being by participation, as follows: 1) that which is something by participation is not this something per se; 2) now, that which is not per se, either is not or is per aliud, 3) Therefore, that which is something by participation is per aliud. In the third and last point, we endeavor to justify the Thomism of our defense and of our emphasis on this notion of “being which is not per se” by considering two texts of St. Thomas. (shrink)

This paper analyzes the role of the principle of causality in Cassirer’s account of the coordination of concepts and spatio-temporal objects. We shall see that, in contradistinction to Kantian schematism, Cassirer maintains that this coordination is not achieved by means of a third element, which albeit intellectual is nevertheless also sensible. Rather, in Cassirer’s view, the coordination will take place through a specification of the concepts that should be sought “within the domain of concepts itself.” We shall show that (...) the principle of causality is the ultimate condition upon which the possibility of the coordination of concepts and spatio-temporal objects depends. (shrink)

The principle of causality has traditionally been formulated in terms of the dynamic equations of classical physics. It has been believed that only stable (conservative) systems can be meaningfully studied in this way. The evolutionary systems studied by social scientists and biologists are non-conservative, and so are excluded from the traditional formulation of causality. The author argues that causal laws for non-conservative systems can be meaningfully formulated. An example based on Malthus'' population theory is given.

In this paper, we will question Heisenberg’s claim that the unpredictability of the experimental findings of quantum physics also affects the self-evident attitude about the universal and necessary nature of the principle of causality. We will try to answer the aforementioned problem by confronting Heisenberg’s criticism of Kant’s position on the universal and necessary/absolute nature of the principle of causality with Kant’s distinction between perceptual and experiential judgments. The distinction between perceptual and experiential judgments reveals that even in (...) Heisenberg’s experimental results that indicate the smallest degree of expectation, predictability, or uniformity of natural events, universal and necessary conditions of knowledge to which Heisenberg denies absolute value still preexist. (shrink)

The argument from causal closure for physicalism requires the principle that a physical event can only occur through being necessitated by antecedent physical events. This article proposes a view of the causal structure of the world that claims not only that an event need not be necessitated by antecedent events, but that an event cannot be necessitated by antecedent events. All events are open to counteraction. In order to spell out various kinds of counteraction I introduce the (...) idea of ‘directedness.’. (shrink)

Some compatibilists believe that the principle of alternative possibilities has been shown to be false by Frankfurt-style arguments, and this gives way to the compatibility of causal determination with moral responsibility. Those incompatibilists who defend the principle of alternative possibilities, on the other hand, insist on the truth of the principle and on the incompatibility of causal determination with moral responsibility. In this article, I argue that Frankfurt-stylecounterexamples are unsuccessful in indicating the falsity of the (...) principle of alternative possibilities, and yet this failure is inconclusive to prove the correctness of incompatibilism. In fact, the principle of alternative possibilities is, I show, compatible with causal determination and thus with compatibilism on a specificunderstanding of determinism and compatibilism. (shrink)

Nancy Cartwright believes that we live in a Dappled World– a world in which theories, principles, and methods applicable in one domain may be inapplicable in others; in which there are no universal principles. One of the targets of Cartwright’s arguments for this conclusion is the Causal Markov condition, a condition which has been proposed as a universal condition on causal structures.1 The Causal Markov condition, Cartwright argues, is applicable only in a limited domain of special cases, (...) and thus cannot be used as a universal principle in causal discovery. I have no dispute with any of these claims here. Rather, I wish to argue for a very limited thesis: that the Causal Markov condition is applicable in the specific domain of microscopic quantum mechanical systems; further, that the condition can fruitfully be applied to the much discussed EPR setup. This is perhaps a surprising conclusion, for it is precisely in this domain that Cartwright’s arguments against the Causal Markov condition have been considered to be the most successful. (shrink)

This paper examines how the metaphysical theses of the Liber de causis are read in De ecclesiastica potestate of Giles of Rome. The hypothesis of this study is that the causal models developed in both texts are different, and therefore the metaphysical theses of the first cannot be the basis for the political theses of the second. Both in the Liber de causis and in De ecclesiastica potestate there is only one causal chain, i.e. in both texts “all (...) power” comes from God. However, this does not imply that in the Liber de causis “the power of all” comes from God. On the contrary, in the Liber de causis, the secondary causes are sovereign in their action. Thus, the political model of the “plenitudo potestatis” - and of expiry of the legal order - built in De ecclesiastica potestate cannot be derived from the metaphysics set out in the Liber de causis. (shrink)

THE ART OF CAUSAL CONJECTURE Glenn Shafer Table of Contents Chapter 1. Introduction........................................................................................ ...........1 1.1. Probability Trees..........................................................................................3 1.2. Many Observers, Many Stances, Many Natures..........................................8 1.3. Causal Relations as Relations in Nature’s Tree...........................................9 1.4. Evidence............................................................................................ ...........13 1.5. Measuring the Average Effect of a Cause....................................................17 1.6. Causal Diagrams..........................................................................................20 1.7. Humean Events............................................................................................23 1.8. Three Levels of Causal Language................................................................27 1.9. An Outline of the Book................................................................................27 Chapter 2. Event Trees............................................................................................... .....31 2.1. Situations and Events...................................................................................32 2.2. The Ordering of Situations and Moivrean Events.......................................35 2.3. Cuts................................................................................................ ..............39 (...) 2.4. Humean Events............................................................................................43 2.5. Moivrean Variables......................................................................................49 2.6. Humean Variables........................................................................................53 2.7. Event Trees for Stochastic Processes...........................................................54 2.8. Timing in Event Trees.................................................................................56 2.9. Intersecting Event Trees...............................................................................60 2.10. Notes on the Literature...............................................................................61 Chapter 3. Probability Trees...........................................................................................63 3.1. Some Types of Probability Trees.................................................................64 ii 3.2. Axioms for the Probabilities of Moivrean Events.......................................68 3.3. Zero Probabilities....................................................................................... ..70 3.4. A Sample-Space Analysis of the Event-Tree Axioms.................................72 3.5. Probabilities and Expected Values for Variables.........................................74 3.6. Martingales......................................................................................... .........79 3.7. The Expectation of a Variable in a Cut........................................................83 3.8. Conditional Expected Value and Conditional Expectation.........................87 Chapter 4. The Meaning of Probability...........................................................................91 4.1. The Interpretation of Expected Value..........................................................92 4.2. The Interpretation of Expectation................................................................95 4.3. The Long Run..............................................................................................98 4.4. Changes in Belief.........................................................................................101 4.5. The Empirical Validation of Probability......................................................106 4.6. The Diversity of Uses of Probability...........................................................108 4.7. Notes on the Literature.................................................................................110 Chapter 5. Independent Events.......................................................................................113 5.1. Independence........................................................................................ .......114 5.2. Weak Independence.....................................................................................118 5.3. The Principle of the Common Cause...........................................................121 5.4. Conditional Independence............................................................................128 5.5. Notes on the Literature.................................................................................133 Chapter 6. Events Tracking Events.................................................................................135 6.1. Tracking............................................................................................ ...........137 6.2. Tracking and Conditional Independence.....................................................142 6.3. Stochastic Subsequence...............................................................................143 6.4. Singular Diagrams for Stochastic Subsequence...........................................147 6.5. Conjunctive and Interactive Forks...............................................................149 Chapter 7. Events as Signs of Events..............................................................................153 iii 7.1. Sign................................................................................................ ..............154 7.2. Weak Sign................................................................................................ ....159 7.3. The Ethics of Causal Talk............................................................................160 7.4. Screening Off...............................................................................................16 2 Chapter 8. Independent Variables...................................................................................167 8.1. Unconditional Independence........................................................................170 8.2. Conditional Independence............................................................................175 8.3. Independence for Partitions.........................................................................177 8.4. Independence for Families of Variables......................................................182 8.5. Individual Properties of the Independence Relations...................................186 Chapter 9. Variables Tracking Variables........................................................................189 9.1. Tracking and Conditional Independence: A Summary...............................190 9.2. Strong Tracking............................................................................................ 192 9.3. Strong Tracking and Conditional Independence..........................................198 9.4. Stochastic Subsequence...............................................................................201 9.5. Functional Dependence................................................................................203 9.6. Tracking in Mean.........................................................................................204 9.7. Linear Tracking............................................................................................ 207 9.8. Tracking by Partitions..................................................................................210 9.9. Tracking by Families of Variables...............................................................212 Chapter 10. Variables as Signs of Variables...................................................................215 10.1. Sign................................................................................................ ............219 10.2. Linear Sign................................................................................................ .222 10.3. Scored Sign................................................................................................ 225 10.4. Families of Variables.................................................................................227 Chapter 11. AnTheory of Event Trees.............................................................229 11.1. Event Trees as Sets of Sets........................................................................230 11.2. Event Trees as Partially Ordered Sets........................................................232 iv 11.3. Regular Event Trees...................................................................................240 11.4. The Resolution of Moivrean Variables......................................................244 11.5. Humean Events and Variables...................................................................246 Chapter 12. Martingale Trees..........................................................................................247 12.1. Examples of Decision Trees......................................................................249 12.2. The Meaning of Probability in a Decision Tree.........................................253 12.3. Martingales......................................................................................... .......257 12.4. The Structure of Martingale Trees.............................................................261 12.5. Probability and Causality...........................................................................265 12.6. Lower and Upper Probability.....................................................................269 12.7. The Law of Large Numbers.......................................................................272 12.8. Notes on the Literature...............................................................................274 Chapter 13. Refining............................................................................................ ...........275 13.1. Examples of Refinement............................................................................277 13.2. A Constructive Definition of Finite Refinement.......................................281 13.3. Axioms for Refinement..............................................................................282 13.4. Lifting Moivrean Events and Variables.....................................................288 13.5. Refining Martingale Trees.........................................................................288 13.6. Grounding........................................................................................... .......294 Chapter 14. Principles of Causal Conjecture..................................................................299 14.1. The Diversity of Causal Explanation.........................................................302 14.2. The Mean Effect of the Happening of a Moivrean Event..........................305 14.3. The Effect of a Humean Variable..............................................................311 14.4. Attribution and Generality.........................................................................316 14.5. The Statistical Measurement of the Effect of a Cause...............................319 14.6. Measurement by Experiment.....................................................................320 14.7. Using Our Knowledge of How Things Work............................................322 v 14.8. Sampling Error...........................................................................................329 14.9. The Sampling Frame..................................................................................329 14.10. Notes on the Literature.............................................................................330 Chapter 15. Causal Models.............................................................................................3 31 15.1. The Causal Interpretation of Statistical Prediction....................................333 15.2. Generalizing to a Family of Exogenous Variables....................................337 15.3 Some Joint Causal Diagrams......................................................................339 15.4. Causal Path Diagrams................................................................................342 15.5. Causal Relevance Diagrams.......................................................................346 15.6. The Meaning of Latent Variables..............................................................352 15.7. Notes on the Literature...............................................................................357 Chapter 16. Representing Probability Trees...................................................................359 16.1. Three Graphical Representations...............................................................361 16.2. Skeletal Simplifications.............................................................................368 16.3. Martingale Trees in Type Theory...............................................................371 Appendix A. Huygens’s Probability Trees.....................................................................379 Huygens’s Manuscript in Translation..................................................................380 Appendix B. Some Elements of Graph Theory..............................................................385 B1. Undirected Graphs........................................................................................385 B2. Directed Graphs............................................................................................38 6 Appendix C. Some Elements of Order Theory...............................................................393 C1. Partial and Quasi Orderings.........................................................................393 C2. Singular and Joint Diagrams for Binary Relations.......................................394 C3. Lattices............................................................................................ .............395 C4. The Lattice of Partitions of a Set..................................................................396 Appendix D. The Sample-Space Framework for Probability.........................................399 D1. Probability Measures....................................................................................399 D2. Variables........................................................................................... ...........400 vi D3. Families of Variables...................................................................................401 D4. Expected Value............................................................................................402 D5. The Law of Large Numbers.........................................................................405 D6. Conditional Probability................................................................................406 D7. Conditional Expected Value........................................................................407 Appendix E. Prediction in Probability Spaces................................................................409 E1. Conditional Distribution...............................................................................411 E2. Regression on a Single Variable...................................................................412 E3. Regression on a Partition or a Family of Variables......................................415 E4. Linear Regression on a Single Variable.......................................................418 E5. Linear Regression on a Family of Variables................................................422 Appendix F. Sample-Space Concepts of Independence.................................................425 F1. Overview............................................................................................ ...........426 F2. Independence Proper.....................................................................................432 F3. Unpredictability in Mean..............................................................................434 F4. Simple Uncorrelatedness..............................................................................437 F5. Mixed Uncorrelatedness...............................................................................438 F6. Partial Uncorrelatedness...............................................................................440 F7. Independence for Partitions..........................................................................442 F8. Independence for Families of Variables.......................................................445 F9. The Basic Role of Uncorrelatedness.............................................................448 F10. Dawid’s Axioms.........................................................................................449 Appendix G. Prediction Diagrams..................................................................................453 G1. Path Diagrams............................................................................................ ..454 G2. Generalized Path Diagrams..........................................................................462 G3. Relevance Diagrams.....................................................................................466 G4. Bubbled Relevance Diagrams......................................................................475 Appendix H. Abstract Stochastic Processes...................................................................479 vii H1. Probability Conditionals and Probability Distributions...............................477 H2. Abstract Stochastic Processes......................................................................479 H3. Embedding Variables and Processes in a Sample Space.............................480 References.......................................................................................... ..............................491. (shrink)

Aristotle’s argument against Timaeus’ view of intellection as an endless circular motion can be divided into two main sections: a categorial one, which rests upon the impossibility of intertwining material and immaterial ingredients into a single (albeit twofold) substance; and a geometrical one, which highlights those features of circular magnitudes which render them unsuitable for performing intellectual tasks. This paper focuses on the first set of reasons that Aristotle puts forward, by stressing the productive philosophical outcome of this otherwise perplexing (...) doxographical survey. It is argued that Aristotle’s own notions of ‘contact’ and ‘unmoved mover’ are greatly indebted to his critical scrutiny of Timaeus’ cosmogony. Further, the paper holds that the version of ‘causal synonymy’ that Aristotle discovers in Timaeus’ εἰκὼς μûθος leads him to recast this principle, in order to avoid unwelcome ‘physiological’ consequences. In this way, Aristotle’s literal reading of Timaeus’ εἰκὼς μûθος emerges as a plausible interpretive stance. (shrink)

'A fascinating book. It contains a sweeping survey of approaches to causation and explanation from the Presocratic philosophers to the Neo-platonist philosophers. Hankinson pays a visit to every major figure and movement in between: the sophists, Plato, Aristotle, the Stoics, the Sceptics, the Epicureans and a variety of medical writers, early and late... impressive... Hankinson's observations are regularly intriguing, at times refreshingly trenchant, and in some cases straightforwardly arresting... the history itself is excellent: clear, intelligently conceived and executed, and broadly (...) accessible. Those in search of a philosophically astute history of clasical philosophy given in terms of one of its own central unifying obsessions will delight in reading R. J. Hankinson's work.' -Christopher Sheilds, Times Literary SupplementR. J. Hankinson traces the history of ancient Greek thinking about causation and explanation. He examines how the Greeks dealt with questions about how and why things happen as and when they do, about the basic constitution and structure of things, about function and purpose, laws of nature, chance, coincidence, and responsibility. Such diverse questions are unified by the fact that they are all demands for explanation, for an account of the world that will render it amenable to prediction and control. (shrink)

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)

This paper analyses the structure of evolutionary theory as a quasi-Newtonian theory and the need to establish a Zero-Cause Law. Several authors have postulated that the special character of drift is because it is the default behaviour or Zero-Cause Law of evolutionary systems, where change and not stasis is the normal state of them. For these authors, drift would be a Zero-Cause Law, the default behaviour and therefore a constituent assumption impossible to change without changing the system. I defend that (...) drift's causal and explanatory power prevents it from being considered as a Zero-Cause Law. Instead, I propose that the default behaviour of evolutionary systems is what I call the Principle of Stasis, which posits that an evolutionary system where there is no selection, drift, mutation, migration, etc., and therefore no difference-maker, will not undergo any change (it will remain in stasis). (shrink)

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. 1. Introduction2. Scattering in Classical Electrodynamics3. Sufficiency of the Physics4. Failure of the Principle of Causality Proposed 4.1. A sometimes principle4.2. The conditions of applicability are obscure4.3. Effects can come before their causes4.4. Vagueness of the relata and of the notion of causal process5. Conclusion. (shrink)

The common cause principle says that every correlation is either due to a direct causal effect linking the correlated entities or is brought about by a third factor, a so-called common cause. The principle is of central importance in the philosophy of science, especially in causal explanation, causal modeling and in the foundations of quantum physics. Written for philosophers of science, physicists and statisticians, this book contributes to the debate over the validity of the common (...) cause principle, by proving results that bring to the surface the nature of explanation by common causes. It provides a technical and mathematically rigorous examination of the notion of common cause, providing an analysis not only in terms of classical probability measure spaces, which is typical in the available literature, but in quantum probability theory as well. The authors provide numerous open problems to further the debate and encourage future research in this field. (shrink)

The Principle of Common Cause (PCC) puts forward the idea that events which occur simultaneously and are correlated have a prior common cause which screens off the correlation. I endorse the view that the PCC does qualify as a principle that can be used as a tool in explaining improbable coincidences. However, though there are epistemological advantages in common cause explanations of correlated events, the PCC is not impeccable. This paper offers a preliminary assessment of the PCC advocated (...) by Reichenbach, and then attempts to illustrate three scenarios in which the principle might be inadequate in explaining correlated events. The paper also compares the Common Cause Principle and the Causal Markov Condition (CMC), and examines the advantages of CMC over the Common Cause Principle. (shrink)

I distinguish three ways in which early modern rationalists seek to apply the principle of sufficient reason to empirical science, and critically assess some of their attempts to do so. I focus especially on how these thinkers assume substantive theories of explanation and intelligibility--which are indebted to the mechanist and experimentalist traditions--in many of their deployments of this rationalist principle. A recurring problem is that these philosophers deploy their standards of intelligibility inconsistently: some of their own favored explanations (...) do not always live up to their standards. I argue that the problem is especially acute for Leibniz and Euler, given their enthusiasm for final-causal explanations. (shrink)

Among the many causes of an event, how do we distinguish the important ones? Are there ways to distinguish among causes on principled grounds that integrate both practical aims and objective knowledge? Psychologist Tania Lombrozo has suggested that causal explanations “identify factors that are ‘exportable’ in the sense that they are likely to subserve future prediction and intervention” (Lombrozo 2010, 327). Hence portable causes are more important precisely because they provide objective information to prediction and intervention as practical aims. (...) However, I argue that this is only part of the epistemology of causal selection. Recent work on portable causes has implicitly assumed them to be portable within the same causal system at a later time. As a result, it has appeared that the objective content of causal selection includes only facts about the causal structure of that single system. In contrast, I present a case study from systems biology in which scientists are searching for causal factors that are portable across rather than within causal systems. By paying careful attention to how these biologists find portable causes, I show that the objective content of causal selection can extend beyond the immediate systems of interest. In particular, knowledge of the evolutionary history of gene networks is necessary for correctly identifying causal patterns in these networks that explain cellular behavior in a portable way. (shrink)

This is a book about Kant's views on causality as understood in their proper historical context. Specifically, Eric Watkins argues that a grasp of Leibnizian and anti-Leibnizian thought in eighteenth-century Germany helps one to see how the critical Kant argued for causal principles that have both metaphysical and epistemological elements. On this reading Kant's model of causality does not consist of events, but rather of substances endowed with causal powers that are exercised according to their natures and circumstances. (...) This innovative conception of Kant's view of causality casts a light on Kant's philosophical beliefs in general, such as his account of temporality, his explanation of the reconciliation of freedom and determinism, and his response to the skeptical arguments of Hume. (shrink)

Translates an important 1932 work by Austrian physicist-turned- philosopher Frank (1884-1966). Among the topics he discusses are the Laplacean determinism of global causal laws of nature; the loss of causal simplicity with the establishment of field concepts; cause and chance in classical, statistical-mechanical, and quantum physics; conservation in laws and causal laws; the seeming irreversibility of natural processes; extremal principles; vitalist explanations as also causal; miracles and theological explanations; and lawfulness in the phenomena of life. First (...) published by Springer-Verlag as Das Kauselgesetz und seine Grenzen. No subject index. Annotation copyrighted by Book News, Inc., Portland, OR. (shrink)

It is not known whether consciousness can affect the physical world, as a result of a free will action or in some other way. To do so, it must be able to produce physical changes that cannot be accounted for by physical laws, an ability we will refer to as causal action, and several issues relevant to this possibility are discussed. 1) Until recently it was thought that the conservation laws of physics would prohibit causal action. It has (...) now been found that such is not the case, but other problems remain. 2) Observations of brain activity show that most of the process for determining a decision is done by the brain. However, the question of whether the final determination is done by mind or the brain is still open. The issue then arises that given the large amount of processing the brain can do, why would mind do this final step and if it does, why would the amount done by mind be so small? (shrink)