Professor Judea Pearl won the 2011 Turing Award "for fundamental contributions to artificial intelligence through the development of a calculus for probabilistic and causal reasoning." This book contains the original articles that led to the award, as well as other seminal works, divided into four parts: heuristic search, probabilistic reasoning, causality, first period (1988-2001), and causality, recent period (2002-2020). Each of these parts starts with an introduction written by Judea Pearl. The volume also contains original, contributed articles by leading (...) researchers that analyze, extend, or assess the influence of Pearl's work in different fields: from AI, Machine Learning, and Statistics to Cognitive Science, Philosophy, and the Social Sciences. The first part of the volume includes a biography, a transcript of his Turing Award Lecture, two interviews, and a selected bibliography annotated by him. (shrink)

This article discusses fundamental problems in "rational choice theory," as outlined by Jon Elster. Elster's discussion of why institutions may not be said to act shows his fundamental presupposition that only "monolithic," unitary entities are capable of action. This is, for him, a reason why only individual human beings may be said to act. Furthermore, human beings may be said to act only insofar as they "maximize" (their "utility") on the basis of a unitary, complete, consistent "preference structure." All action (...) that is not maximization in this sense is for Elster not really human action, but rather instances of "pure causality." Elster distinguishes between the "real," intentional person, who "maximizes," and "purely causal forces" within the person. This article tries to show that this radical, sharp dichotomy between "intentionality," in this narrow sense, and "pure causality" is inadequate as a basis for understanding human action. This radical dichotomy is central to important arguments made by Elster more generally. (shrink)

ABSTRACT Causes must be distinct from their effects. If the temperature in a room is 15°F, this can cause water pipes to freeze. However, the temperature’s being 15°F is not a cause of the temperature’s being below the freezing point. In general, conceptual, logical, mathematical, and other non-causal dependence relations should not be misclassified as causal. In this paper, I discuss how interventionist theories of causation can meet the challenge of distinguishing between (direct or indirect) causal relations (...) and dependence relations that are due to non-causal factors. I survey several approaches to solving this problem, and propose a novel criterion based on the notion of supervenience. According to this criterion, variables are sufficiently distinct to qualify as the relata of a causal relation iff they do not have overlapping minimal supervenience bases. (shrink)

Any low-level processes, the sequence of chemical interactions in a living cell, muscle cellular activity, processor commands or neuron interaction, is possible only if there is a downward causality, only due to uniting and controlling power of the highest level. Therefore, there is no special “hard problem of consciousness”, i.e. the problem of relation of ostensibly purely biological materiality and non-causal mentality - we have only the single philosophical problem of relation between the upward and downward causalities, the problem (...) of interrelation between hierarchic levels of existence. It is necessary to conclude that the problem of determinacy of chemical processes by the biological ones and the problem of neuron interactions caused by consciousness are of one nature and must have one solution. (shrink)

Research on the nature of dispositionality or causal power has flourished in recent years in metaphysics. This trend has slowly begun to influence debates in the philosophy of agency, especially in the literature on free will. Both sophisticated versions of agent-­‐causalism and the new varieties of dispositionalist compatibilism exploit recently developed accounts of dispositionality in their defense. In this paper, I examine recent work on agent-­‐causal power, focusing primarily on the account of agent-­‐causalism developed and defended by (...) Timothy O’Connor’s in his work on free will. Assuming the existence of irreducible causal powers, I offer an. (shrink)

Ernst Mayr’s classical work on the nature of causation in biology has had a huge influence on biologists as well as philosophers. Although his distinction between proximate and ultimate causation recently came under criticism from those who emphasize the role of development in evolutionary processes, the formal relationship between these two notions remains elusive. Using causal graph theory, this paper offers a unified framework to systematically translate a given “proximate” causal structure into an “ultimate” evolutionary response, and (...) illustrates evolutionary implications of various kinds of causal mechanisms including epigenetic inheritance, maternal effects, and niche construction. These results not only reveal the essential interplay between proximate and ultimate causation in the study of evolution, but also provide a formal method to evaluate or discover non-standard or yet unknown evolutionary phenomena. (shrink)

Causality in Macroeconomics examines causality while taking macroeconomics seriously. A pragmatic and realistic philosophy is joined to a macroeconomic foundation that refines Herbert Simon's well-known work on causal order to make a case for a structural approach to causality. The structural approach is used to understand modern rational expectations models, regime switching models, Granger causality, vector autoregressions, the Lucas critique, and concept exogeneity. Techniques of causal inference based on patterns of stability and instability in the face of (...) identified regime changes are developed and illustrated in two empirical case studies. (shrink)

It has recently been argued that normative considerations play an important role in causal cognition. For instance, when an agent violates a moral rule and thereby produces a negative outcome, she will be judged to be much more of a cause of the outcome, compared to someone who performed the same action but did not violate a norm. While there is a substantial amount of evidence reporting these effects, it is still a matter of debate how this evidence is (...) to be interpreted. In this paper, we engage with the three most influential classes of explanations, namely, (a) the Norm‐Sensitive Cognitive Process View, (b) the Normative Concept View, and (c) the Pragmatics View. We will outline how these theories explain the empirical results and in what ways they differ. We conclude with a reflection on how well these strategies do overall and what questions they still leave unanswered. (shrink)

What do the words "ceteris paribus" add to a causal hypothesis, that is, to a generalization that is intended to articulate the consequences of a causal mechanism? One answer, which looks almost too good to be true, is that a ceteris paribus hedge restricts the scope of the hypothesis to those cases where nothing undermines, interferes with, or undoes the effect of the mechanism in question, even if the hypothesis's own formulator is otherwise unable to specify fully what (...) might constitute such undermining or interference. I will propose a semantics for causal generalizations according to which ceteris paribus hedges deliver on this promise, because the truth conditions for a causal generalization depend in part on the—perhaps unknown—nature of the mechanism whose consequences it is intended to describe. It follows that the truth conditions for causal hypotheses are typically opaque to the very scientists who formulate and test them. (shrink)

This work consists of two parts. Part I will be a contribution to a philo- sophical discussion of the nature of causal explanation. It will present my contrastive counterfactual theory of causal explanation and show how it can be used to deal with a number of problems facing theories of causal explanation. Part II is a contribution to a discussion of the na- ture of interest explanation in social studies of science. The aim is to help (...) to resolve some controversies concerning interest explanation by explicating the concept of interest and its explanatory uses by using the account of explanation developed in Part I. (shrink)

We use concepts of causal powers and their relatives-dispositions, capacities, and abilities-to describe the world around us, both in everyday life and in scientific practice. This volume presents new work on the nature of causal powers, and their connections with other phenomena within metaphysics, philosophy of science, and philosophy of mind.

The verbs cause, enable, and prevent express beliefs about the way the world works. We offer a theory of their meaning in terms of the structure of those beliefs expressed using qualitative properties of causal models, a graphical framework for representing causal structure. We propose that these verbs refer to a causal model relevant to a discourse and that “A causes B” expresses the belief that the causal model includes a link from A to B. “A (...) enables/allows B” entails that the model includes a link from A to B, that A represents a category of events necessary for B, and that an alternative cause of B exists. “A prevents B” entails that the model includes a link from A to B and that A reduces the likelihood of B. This theory is able to account for the results of four experiments as well as a variety of existing data on human reasoning. (shrink)

Resumen El presente estudio investiga la importancia de la “experiencia originaria” husserliana para la comprensión del conflicto de las ciencias exactas con el problema de Dios, que Edith Stein califica de “angustia inconsciente de encontrarse” con Él. A través de su controversia con la Psicología del siglo XIX, la discípula de Husserl muestra en su obra Causalidad Psíquica que hace falta un adecuado conocimiento de la índole propia de la ciencia para remontar con rigor metódico a la originariedad de la (...) experiencia humana y el Dios presente allí. Esto conducirá a la ciencia al descubrimiento de su propia identidad por la superación del bloqueo consciente o inconsciente ante la revelación de Dios en el alma humana. De ahí que el presente estudio ofrezca un aporte relevante para la dilucidación filosófica de una cuestión teológica candente hoy ―aporte logrado a través de un esfuerzo interdisciplinario arduo.The present study investigates the importance of the Husserlian “originary experience” for the understanding of the conflict of the exacts sciences with the problem of God, which Edith Stein describes as “unconscious anguish of being” with Him. Through her controversy with of the 19th century Psychology, the disciple of Husserl shows in her work Psychic Causality that it is necessary to have an adequate knowledge of the nature of science to trace with methodical rigor the originarity of the human experience and the God present in there. This will conduct science to a discovery of its own proper identity by the overcoming of the unconscious or conscious blocking before the revelation of God in the human soul Hence, the present study offers a relevant contribution to the philosophical elucidation of a burning theological question today -a contribution achieved through an arduous interdisciplinary effort. (shrink)

In this chapter, we outline the range of argument forms involving causation that can be found in everyday discourse. We also survey empirical work concerned with the generation and evaluation of such arguments. This survey makes clear that there is presently no unified body of research concerned with causal argument. We highlight the benefits of a unified treatment both for those interested in causal cognition and those interested in argumentation, and identify the key challenges that must be (...) met for a full understanding of causal argumentation. (shrink)

In this work we try to study theories of causation based upon causal processes and causal interactions in the context of classical and quantum physics. Our central aim is to find out whether such causal theories are compatible with the world picture suggested by contemporary theories of physics. In the first part, we review, compare and try to place among more general taxonomical schemes, the causal theories by Russell (the causal lines approach), Reichenbach (mark (...) method, probabilistic causality and the principle of common cause), Salmon (mark method, structure, invariant and conserved quantities approaches) and Dowe (conserved quantity theory). In the second part of the work, we deal with some problems that process theories of causation face whenever one tries to define, rigorously, causal concepts (e.g. “causal process” or “conjunctive common cause”) in the context of classical relativistic physical theories (van Dam – Wigner particle theory, the free Klein-Gordon field, classical electromagnetic theory) as well as quantum theories (algebraic relativistic quantum field theory). In this vein, special attention has been given to the locality problem in physical theories as it emerges from the structure of local classical the quantum theories (semantic locality), the relation between global and local conditions for physical systems (global and local determinism) as well as the problem of action at a distance, which is of central importance for the theories of causation under examination (Bell’s inequalities, EPR paradox, Reeh-Schlieder theorem and local independence conditions). We argue for three different theses: first, there are physical theories in the context of which it is impossible even to formulate the necessary physical hypotheses that allow us to define basic causal concepts and causal principles; second, there are physical theories which allow us to formulate these hypotheses only ad hoc; finally, in some cases it is necessary to revise basic causal principles and concepts bequeathed by the philosophical tradition. (shrink)

One of Stuart Glennan's most prominent contributions to the new mechanist debate consists in his reductive analysis of higher-level causation in terms of mechanisms (Glennan, 1996). In this paper I employ the causal Bayes net framework to reconstruct his analysis. This allows for specifying general assumptions which have to be satis ed to get Glennan's approach working. I show that once these assumptions are in place, they imply (against the background of the causal Bayes net machinery) that higher-level (...) causation indeed reduces to interactions between component parts of mechanisms. I also briefly discuss the plausibility of these assumptions and some consequences for the mechanism debate. (shrink)

Given their physical realization, what causal work is left for functional properties to do? Humean solutions to the exclusion problem (e.g. overdetermination and difference-making) typically appeal to counterfactual and/or nomic relations between functional property-instances and behavioural effects, tacitly assuming that such relations suffice for causal work. Clarification of the notion of causal work, I argue, shows not only that such solutions don't work, but also reveals a novel solution to the exclusion problem based (...) on the relations between dispositional properties at different levels of mechanism, which involves three central claims: (i) the causal work of properties consists in grounding dispositions, (ii) functional properties are dispositions, and (iii) the dispositions of mechanisms are grounded in the dispositions of their components. Treating functional mental properties as dispositions of components in psychological mechanisms, I argue that such properties do the causal work of grounding agent-level dispositions. These dispositions, while ultimately grounded in the physical realizers of mental properties, are indirectly so grounded, through a hierarchy of grounding relations that extends upwards, of necessity, through the mental domain. (shrink)

In my field, animal developmental biology, and in what could be regarded as its “deep time derivative,” the evolutionary biology of the animal body plan, there exist two kinds of experimentally supported causal explanation. These can be described as “rooted” and “unrooted.” Rooted causal explanation provides logical links to and from the genomic regulatory code, extending right into the genomic sequences that control regulatory gene expression. The genomic regulatory code ultimately determines the developmental process in a direct way, (...) since subsequent developmental events all depend directly and specifically on what regulatory genes are being expressed and where in the developing organism they are being expressed.. (shrink)

The existence of causal explanations in science has been an issue of interest in Western philosophy from its very beginnings. That is the reason this work, following an idea of Mario Bunge, makes a historical review of this matter. The modern treatment of this subject takes place since the postulation by Popper and Hempel of the D-N model of scientific explanation, whose viability is scrutinized here from different points of view in the current philosophy of science. The main (...) object of this paper is to present two arguments against the possibility of causal explanations in theoretical physics. The first one concerns the existence, in certain cases, of inter-theoretical incompatibilities, and the second refers to the need to resort, in other cases, to concatenations of laws of different theories and disciplines. The final conclusion will be the defence of a form of theoretical explanation, which follows the Popper-Hempel model, but devoid of any ontological and metaphysical connotations. (shrink)

This paper seeks to show how a proper version of dispositionalism, taking powers as basic elements of reality, may figure as a causal theory of perception. On the basis of this argument, the paper draws some further conclusions, which may indicate the explanatory competence of such a disposition-affine theory of causality, for example in terms of the distinction between primary and secondary qualities, as well as an ontological account of mere potentialities of active capacities to perceive and of “privations,” (...) respectively. (shrink)

Why is understanding causation so important in philosophy and the sciences? Should causation be defined in terms of probability? Whilst causation plays a major role in theories and concepts of medicine, little attempt has been made to connect causation and probability with medicine itself. Causality, Probability, and Medicine is one of the first books to apply philosophical reasoning about causality to important topics and debates in medicine. Donald Gillies provides a thorough introduction to and assessment of competing theories of causality (...) in philosophy, including action-related theories, causality and mechanisms, and causality and probability. Throughout the book he applies them to important discoveries and theories within medicine, such as germ theory; tuberculosis and cholera; smoking and heart disease; the first ever randomized controlled trial designed to test the treatment of tuberculosis; the growing area of philosophy of evidence-based medicine; and philosophy of epidemiology. This book will be of great interest to students and researchers in philosophy of science and philosophy of medicine, as well as those working in medicine, nursing and related health disciplines where a working knowledge of causality and probability is required. (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)

Not all scientific explanations work by describing causal connections between events or the world's overall causal structure. In addition, mathematicians regard some proofs as explaining why the theorems being proved do in fact hold. This book proposes new philosophical accounts of many kinds of non-causal explanations in science and mathematics.

D. M. Armstrong is an eminent Australian philosopher whose work over many years has dealt with such subjects as: the nature of possibility, concepts of the particular and the general, causes and laws of nature, and the nature of human consciousness. This collection of essays explores the many facets of Armstrong's work, concentrating on his more recent interests. There are four sections to the book: possibility and identity, universals, laws and causality, and philosophy of mind. The contributors comprise (...) an international group of philosophers from the United States, England and Australia. An interesting feature of the volume is that Armstrong himself has written responses to each of the essays. There is also a complete bibliography of Armstrong's writings. (shrink)

Not only has the philosophical debate on causation been gaining ground in the last few decades, but it has also increasingly addressed the sciences. The biomedical sciences are among the most prominent fields that have been considered, with a number of works tackling the understanding of the notion of cause, the assessment of genuinely causal relations and the use of causal knowledge in applied contexts. Far from denying the merits of the debate on causation and the major theories (...) it comprises, this paper is meant as a stimulus for theorists of causation in the philosophy of biomedicine, with a focus on clinical matters. Without aiming at putting forward an original theory of causation and starting from the narration of two actual but paradigmatic cases at the joints between biomedical research and clinical practice, we want to point out that some pathological situations addressed by molecular medicine actually prove resistant to some of our major epistemological accounts of causal explanation. Given this scenario, which is very frequent in our hospitals, our analysis aims to provide a stimulus for the debate among theorists of causation in biomedicine interested in real cases in science in practice. We believe that this might in turn encourage some more general rethinking of the complex intertwinement of science, philosophy of science and ethics, as well as of the role of philosophy of science for clinical medicine itself. (shrink)

Recent, well-publicized accounting scandals have shown that the penalties outsiders impose on those found culpable of earnings management can be severe. However, less is known about how colleagues within internal labor markets respond when they believe fellow managers have managed earnings. Designers of responsibility accounting systems need to understand the reputational costs managers impose on one another within internal labor markets. In an experimental study, 159 evening MBA students were asked to assume the role of a manager in a company (...) and respond to a scenario in which another manager (the target manager) has the opportunity to engage in earnings management. Participants provided causal attributions, assessed the morality of the target manager, and indicated whether they would change their judgments about the target manager's reputation. The study manipulated three between-subjects factors: (1) whether the target manager chose to engage in earnings management, (2) whether the company's budgetary control system was rigid or flexible, and (3) whether the target manager's work history was average or above average. We found that causal attributions are affected more by the budgetary systems when the target did not manage earnings than when the manager did. We also found that morality judgments were significantly associated with the target manager's behavior, but not with the budgetary system. In addition, participants' judgments about the target manager's reputation were more strongly associated with morality judgments than with causal attributions. We discuss implications of the role of reputation in management control systems design. (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 grants perceptible qualities the power to generate sense perception in animals. But it is unclear whether, for him, these qualities can produce any effect other than perception. In this paper I address this issue through a novel approach. To show that they can produce non-perceptual effects, I explore contexts in his extant works where qualities appear to do causal work in nature without leading to perception in animals. This inquiry aims to demonstrate that Aristotle’s realism about qualities (...) survives a potential threat posed by views that dispute the qualities’ causal efficacy outside perception. (shrink)

Frank Jackson and Philip Pettit have defended a non-reductive account of causal relevance known as the ‘program explanation account’. Allegedly, irreducible mental properties can be causally relevant in virtue of figuring in non-redundant program explanations which convey information not conveyed by explanations in terms of the physical properties that actually do the ‘causal work’. I argue that none of the possible ways to spell out the intuitively plausible idea of a program explanation serves its purpose, viz., defends (...) non-reductive physicalism against Jaegwon Kim’s Causal Exclusion Argument according to which non-reductive physicalism is committed to epiphenomenalism because irreducible mental properties are ‘screened off’ from causal relevance by their physical realizers. Jackson and Pettit’s most promising explication of a program explanation appeals to the idea of invariance of effect under variation of realization , but I show that invariance of effect under variation of realization is neither necessary nor sufficient for causal relevance. (shrink)

Knowledge of causal laws is expensive and hard to come by. But we work hard to get it because we believe that it will reduce contingency in planning policies and in building new technologies: knowledge of causal laws allows us to predict reliably what the outcomes will be when we manipulate the factors cited as causes in those laws. Or do they? This paper will argue that causal laws have no special role here. As economists from (...) JS Mill to Robert Lucas and David Hendry stress, along recently with philosophers like James Woodward and Sandra Mitchell, they can do the job only if they are invariant under the manipulations proposed. But then, I shall argue, anything that is invariant under the proposed manipulations will do this job equally well. There seems to be nothing special about causal-law knowledge in and of itself that makes it particularly valuable for policy and technology prediction. What seems to matter is invariance alone, not causality. But what guarantees invariance and how do we know when it will obtain? Here certain kinds of causal laws do have a special place – those underwritten either by what I have called ‘nomological machines’ or by what I have called ‘capacities’. Capacities and nomological machines have a double virtue that makes them invaluable for policy planning. First, the causal laws they give rise to will be invariant so long as they obtain; and second, they typically have visible markers we can come to recognize that tell us when they obtain. The markers for nomological machines are shakier than those for capacities, though, since capacities are often tied to markers by well-established empirical laws. Capacities have their own drawback however, which is the final topic of this paper: the causal laws that are guaranteed by a capacity connect the obtaining (or triggering) of a capacity with its exercise. But Hume argued (mistakenly I suggest) that no distinction can be made between the obtaining of a power and its exercise. (shrink)

Causal overdetermination occupies an uncomfortable place within all the major theories of causation. A natural solution to the problems it gives rise to would be to resolve overdetermination into preemption or joint causation. However, such a solution would seem to lead to individuate events in a fragile manner. The issue of such modal fragility is addressed and it is argued that events designated as effects are always fragile in a natural way and the putative problems of adopting modal fragility (...) can be resolved. It is also studied whether causal overdetermination could work in a setting where the overdetermining causes occur in different levels of reality. Typically postulated higher-level causes are shown to contradict the definitional features of overdetermination. There is thus no reasonable role for causal overdetermination and the notion should be abandoned. (shrink)

This article describes research pursued by members of the McDonnell Collaborative on Causal Learning. A number of members independently converged on a similar idea: one of the central functions served by claims of actual causation is to highlight patterns of dependence that are highly portable into novel contexts. I describe in detail how this idea emerged in my own work and also in that of the psychologist Tania Lombrozo. In addition, I use the occasion to reflect on the (...) nature of interdisciplinary collaboration in general and on the interaction between philosophy and psychology in particular. (shrink)

This paper outlines a realist approach to the social ontology of discourse. It seeks to synthesise some elements of the approach to discourse found in the early work of Michel Foucault with a critical realist understanding of the causal power of social structures. It will argue that discursive structures can be causally significant when they are normatively endorsed and enforced by specific groups of people; that it is not discourse as such but these groups—discursive circles—that are causally effective; (...) and that such an account allows us to reconcile the role of discourse with that of the subject. (shrink)

Philosophers of social science have emphasized mechanistic approaches to causal inquiry for some time now, showing why focusing on the mechanisms behind correlations is preferable to focusing on correlations alone (cf. Johnson 2006, Little 1991, Reiss 2007, 2009, Steel 2004, see also King, Keohane, and Verba 1994 for an example of purely correlational research). In Process Tracing: from Metaphor to Analytic Tool, political scientists Andrew Bennett and Jeffrey Checkel present a concrete method for finding evidence of causal mechanisms, (...) process tracing. Process tracers give evidence for causal relations in terms of the observable implications of the underlying causal mechanisms through which a putative cause affects some effect of interest. Such observable implications often take the form of a chain of events, or process, which connects cause and effect. Though Process Tracing contains applications of mechanistic reasoning unfamiliar to philosophers, and as such will be of interest to those working in the mechanist tradition, Bennett and Checkel’s own discussion of the philosophical foundations of process tracing is limited. The reader will have to delve deep into the volume’s contributed chapters for links between the literature on causation and this new method. (shrink)

The evolutionary emergence of biological processes in organisms with inner, qualitative aspects has not been explained in any sufficient way by neurobiology, nor by the traditional neo-Darwinian paradigm — natural selection would appear to work just as well on insentient zombies (with the right behavioral input-output relations) as on real sentient animals. In consciousness studies one talks about the ‘hard problem’ of qualia. In this paper I sketch a set of principles about sign action, causality and emergent evolution. On (...) the basis of these principles, I characterize a concept of cause that would allow for a naturalistic explanation of the origin of consciousness. The suggested account of causation also turns the ‘hard problem’ of qualia into the easier problem of relating experimental biology to experiential biology. (shrink)

Knowledge of causal laws is expensive and hard to come by. But we work hard to get it because we believe that it will reduce contingency in planning policies and in building new technologies: knowledge of causal laws allows us to predict reliably what the outcomes will be when we manipulate the factors cited as causes in those laws. Or do they? This paper will argue that causal laws have no special role here. As economists from (...) JS Mill to Robert Lucas and David Hendry stress, along recently with philosophers like James Woodward and Sandra Mitchell, they can do the job only if they are invariant under the manipulations proposed. But then, I shall argue, anything that is invariant under the proposed manipulations will do this job equally well. There seems to be nothing special about causal-law knowledge in and of itself that makes it particularly valuable for policy and technology prediction. What seems to matter is invariance alone, not causality. But what guarantees invariance and how do we know when it will obtain? Here certain kinds of causal laws do have a special place – those underwritten either by what I have called ‘nomological machines’ or by what I have called ‘capacities’. Capacities and nomological machines have a double virtue that makes them invaluable for policy planning. First, the causal laws they give rise to will be invariant so long as they obtain; and second, they typically have visible markers we can come to recognize that tell us when they obtain. The markers for nomological machines are shakier than those for capacities, though, since capacities are often tied to markers by well-established empirical laws. Capacities have their own drawback however, which is the final topic of this paper: the causal laws that are guaranteed by a capacity connect the obtaining (or triggering) of a capacity with its exercise. But Hume argued (mistakenly I suggest) that no distinction can be made between the obtaining of a power and its exercise. (shrink)

Aristotle grants perceptible qualities the power to generate sense perception in animals. But it is unclear whether, for him, these qualities can produce any effect other than perception. In this paper, I address this issue through a novel approach. To show that they can produce non-perceptual effects, I explore contexts in his extant works where qualities appear to do causal work in nature without leading to perception in animals. This inquiry aims to demonstrate that Aristotle’s realism about qualities (...) survives a potential threat posed by views that dispute the qualities’ causal efficacy outside perception. (shrink)

objects are standardly taken to be causally inert, but this claim is rarely explicitly argued for. In the context of his platonism about musical works, in order for musical works to be audible, Julian Dodd argues that abstracta are causally efficacious in virtue of their concrete tokens participating in events. I attempt to provide a principled argument for the causal inertness of abstracta by first rejecting Dodd’s arguments from events, and then extending and generalizing the causal exclusion argument (...) to the abstract/concrete distinction. For reasons of parsimony, if concrete tokens or instantiations of abstract objects account for all causal work, then there is no reason to attribute causal efficacy to abstracta, and thus reason to maintain their causal inertness. I then consider how one of the main arguments against causal exclusion, namely Stephen Yablo’s notion of “proportionality”, could be modified to support the causal efficacy of abstracta. I argue that from a few simple premises Yablo’s account in fact supports their causal inertness. Having a principled reason for the causal inertness of abstracta appears to entail that the musical platonist must admit that we never literally hear the musical work, but only its performances. I sketch a solution to this problem available to Dodd, so that the musical platonist can maintain that musical works are abstract objects and are causally inert while retaining their audibility. (shrink)

The causal problem has become topical once again. While we are no longer causalists or believers in the universal truth of the causal principle we continue to think of causes and effects, as well as of causal and noncausal relations among them. Instead of becoming indeterminists we have enlarged determinism to include noncausal categories. And we are still in the process of characterizing our basic concepts and principles concerning causes and effects with the help of exact tools. (...) This is because we want to explain, not just describe, the ways of things. The causal principle is not the only means of understanding the world but it is one of them. The demand for a fourth edition of this distinguished book on the subject of causality is clear evidence that this principle continues to be an important and popular area of philosophic enquiry. Non-technical and clearly written, this book focuses on the ontological problem of causality, with specific emphasis on the place of the causal principle in modern science. Mario Bunge first defines the terminology employed and describes various formulations of the causal principle. He then examines the two primary critiques of causality, the empiricist and the romantic, as a prelude to the detailed explanation of the actual assertions of causal determinism. Bunge analyzes the function of the causal principle in science, touching on such subjects as scientific law, scientific explanation, and scientific prediction. In so doing, he offers an education to layman and specialist alike on the history of a concept and its opponents. Professor William A. Wallace, author of Causality and Scientific Explanation said of an earlier edition of this work: "I regard it as a truly seminal work in this field.". (shrink)

Orthodox physicalism has a problem with mental causation. If physics is complete and mental events are not identical to physical events (as multiple-realisation arguments imply) it seems as though there is no causal work for the mental to do. This paper examines some recent attempts to overcome this problem by analysing causation in terms of counterfactuals or conditional probabilities. It is argued that these solutions cannot simultaneously capture the force of the completeness of physics and make room for (...) mental causation. (shrink)

The philosophical theory of scientific explanation proposed here involves a radically new treatment of causality that accords with the pervasively statistical character of contemporary science. Wesley C. Salmon describes three fundamental conceptions of scientific explanation--the epistemic, modal, and ontic. He argues that the prevailing view is untenable and that the modal conception is scientifically out-dated. Significantly revising aspects of his earlier work, he defends a causal/mechanical theory that is a version of the ontic conception. Professor Salmon's theory furnishes (...) a robust argument for scientific realism akin to the argument that convinced twentieth-century physical scientists of the existence of atoms and molecules. To do justice to such notions as irreducibly statistical laws and statistical explanation, he offers a novel account of physical randomness. The transition from the "reviewed view" of scientific explanation to the causal/mechanical model requires fundamental rethinking of basic explanatory concepts. (shrink)

Philosophers of mind have long been interested in the relation between two ideas: that causality plays an essential role in our understanding of the mental; and that we can gain an understanding of belief and desire by considering the ascription of attitudes to people on the basis of what they say and do. Many have thought that those ideas are incompatible. William Child argues that there is in fact no tension between them, and that we should accept both. He shows (...) how we can have a causal understanding of the mental without having to see attitudes and experiences as internal, causally interacting entities and he defends this view against influential objections. The book offers detailed discussions of many of Donald Davidson's contributions to the philosophy of mind, and also considers the work of Dennett, Anscombe, McDowell, and Rorty, among others. Issues discussed include: the nature of intentional phenomena; causal explanation; the character of visual experience; psychological explanation; and the causal relevance of mental properties. (shrink)

I extend work from Krakauer et al. (2020), who propose a conception of individuality as the capacity to propagate information through time. From this conception, they develop information-theoretic measures. I identify several shortcomings with these measures—in particular, that they are associative rather than causal. I rectify this shortcoming by deriving a causal information-theoretic measure of individuality. I then illustrate how this measure can be implemented and extended in the context of evolutionary transitions in individuality.

This work presents a causal conception of metaphysical modality in which a state of affairs is metaphysically possible if and only if it can be caused (in the past, the present or the future) by current entities. The conception is contrasted with what is called the "combinatorial" conception of modality, in which everything can coexist with anything else. This work explains how the notion of 'causality' should be construed in the causal theory, what difference exists between (...) modalities thus defined from nomological modality, how accessibility relations between possible worlds should be interpreted, and what is the relation between the causal conception and the necessity of origin. (shrink)

Intervening on causal systems can illuminate their underlying structures. Past work has shown that, relative to adults, young children often make intervention decisions that appear to confirm a single hypothesis rather than those that optimally discriminate alternative hypotheses. Here, we investigated how the ability to make informative causal interventions changes across development. Ninety participants between the ages of 7 and 25 completed 40 different puzzles in which they had to intervene on various causal systems to determine (...) their underlying structures. Each puzzle comprised a three‐ or four‐node computer chip with hidden wires. On each trial, participants viewed two possible arrangements of the chip's hidden wires and had to select a single node to activate. After observing the outcome of their intervention, participants selected a wire configuration and rated their confidence in their selection. We characterized participant choices with a Bayesian measurement model that indexed the extent to which participants selected nodes that would best disambiguate the two possible causal structures versus those that had high causal centrality in one of the two causal hypotheses but did not necessarily discriminate between them. Our model estimates revealed that the use of a discriminatory strategy increased through early adolescence. Further, developmental improvements in intervention strategy were related to changes in the ability to accurately judge the strength of evidence that interventions revealed, as indexed by participants' confidence in their selections. Our results suggest that improvements in causal information‐seeking extend into adolescence and may be driven by metacognitive sensitivity to the efficacy of previous interventions in discriminating competing ideas. (shrink)