Causal Reasoning, Misc

Bradford Hill (1965) highlighted nine aspects of the complex evidential situation a medical researcher faces when determining whether a causal relation exists between a disease and various conditions associated with it. These aspects are widely cited in the literature on epidemiological inference as justifying an inference to a causal claim, but the epistemological basis of the Hill aspects is not understood. We offer an explanatory coherentist interpretation, explicated by Thagard's ECHO model of explanatory coherence. The ECHO model captures the complexity (...) of epidemiological inference and provides a tractable model for inferring disease causation. We apply this model to three cases: the inference of a causal connection between the Zika virus and birth defects, the classic inference that smoking causes cancer, and John Snow’s inference about the cause of cholera. (shrink)

In a recent discussion note Sober (1985) elaborates on the argument given in Sober (1982) to show the inadequacy of Ronald Giere's (1979, 1980) causal model for cases of frequency-dependent causation, and denies that Giere's (1984) response avoids the problem he raises. I argue that frequency-dependent effects do not pose a problem for Giere's original causal model, and that all parties in this dispute have been guity of misinterpreting the counterfactual populations involved in applying Giere's model.

We present a logic, $$\mathbf {ELI^r}$$ ELI r, for the discovery of deterministic causal regularities starting from empirical data. Our approach is inspired by Mackie’s theory of causes as INUS-conditions, and implements a more recent adjustment to Mackie’s theory according to which the left-hand side of causal regularities is required to be a minimal disjunction of minimal conjunctions. To derive such regularities from a given set of data, we make use of the adaptive logics framework. Our knowledge of deterministic causal (...) regularities is, as Mackie noted, most often gappy or elliptical. The adaptive logics framework is well-suited to explicate both the internal and the external dynamics of the discovery of such gappy regularities. After presenting $$\mathbf {ELI^r}$$ ELI r, we first discuss these forms of dynamics in more detail. Next, we consider some criticisms of the INUS-account and show how our approach avoids them, and we compare $$\mathbf {ELI^r}$$ ELI r with the CNA algorithm that was recently proposed by Michael Baumgartner. (shrink)

There is, among some scientists and philosophers, the idea that any theory that would allow the time travel would introduce causal issues. These types of temporal paradoxes can be avoided by the Novikov self-consistency principle or by a variation in the interpretation of many worlds with interacting worlds. The world in which we live has, according to David Lewis, a Parmenidean ontology: "a manifold of events in four dimensions," and the occupants of the world are the 4-dimensional aggregates of the (...) stages - "temporal lines". The causal loops in backwards time travel involve events that appear to "come from nowhere," paradoxical "self-existent" objects or information, resulting in a bootstrap paradox. Many believe that causality loops are not impossible or unacceptable, but only inexplicable. DOI: 10.13140/RG.2.2.28792.70407. (shrink)

This paper defends strong proportionality against what I take to be its principal objection – that proportionality fails to preserve common sense causal intuitions – by articulating independently plausible constraints on how to represent causal situations. I first assume an interventionist formulation of proportionality, following Woodward. This views proportionality as a relational constraint on variable selection in causal modeling that requires that changes in the cause variable line up with those in the effect variable. I then argue that the principal (...) objection derives from a failure to recognize two constraints on variable selection presupposed by interventionism: exhaustivity and exclusivity. (shrink)

Making a Difference presents fifteen original essays on causation and counterfactuals by an international team of experts. Collectively, they represent the state of the art on these topics. The essays in this volume are inspired by the life and work of Peter Menzies, who made a difference in the lives of students, colleagues, and friends. Topics covered include: the semantics of counterfactuals, agency theories of causation, the context-sensitivity of causal claims, structural equation models, mechanisms, mental causation, causal exclusion argument, free (...) will, and the consequence argument. (shrink)

Before I respond to James G. Matlock’s comments on my coedited volume, The Myth of an Afterlife: The Case against Life After Death (MoA), I would like to thank him for taking the time to review such a large volume—and review it conscientiously—even if we ultimately disagree about its import. I would also like to extend my thanks to Journal of Parapsychology editor John Palmer for inviting this response, as it gives me an opportunity to clarify why many secondary issues (...) (and some significant matters) were untouched in the volume. While I find most of Matlock’s complaints specious, they nevertheless underscore the need to clear up misconceptions about the collection’s purpose, some of which are also found in others’ reviews. -/- 1. Poisoning the Well 2. Mind-Brain Correlations are Data that Test Hypotheses 3. The Prior Probability Diversion 4. Apparent Causal Closure is Incidental Corroboration 5. How Relevant is the Metaphysics of Mind? 6. Theological Critiques do not Assess Survival Research 7. How Much Survival Research Ought One Critique? (shrink)

A model of a Markov process is presented in which observing the present state of a system is asymmetrically related to inferring the system's future and inferring its past. A likelihood inference about the system's past state, based on observing its present state, is justified no matter what the parameter values in the model happen to be. In contrast, a probability inference of the system's future state, based on observing its present state, requires further information about the parameter values.

Newton’s Regulae philosophandi—the rules for reasoning in natural philosophy—are maxims of causal reasoning and induction. This essay reviews their significance for Newton’s method of inquiry, as well as their application to particular propositions within the Principia. Two main claims emerge. First, the rules are not only interrelated, they defend various facets of the same core idea: that nature is simple and orderly by divine decree, and that, consequently, human beings can be justified in inferring universal causes from limited phenomena, if (...) only fallibly. Second, the rules make substantive ontological assumptions on which Newton’s argument in the Principia relies. (shrink)

This chapter explores the idea that the need to establish common knowledge is one feature that makes social cognition stand apart in important ways from cognition in general. We develop this idea on the background of the claim that social cognition is nothing but a type of causal inference. We focus on autism as our test-case, and propose that a specific type of problem with common knowledge processing is implicated in challenges to social cognition in autism spectrum disorder (ASD). This (...) problem has to do with the individual’s assessment of the reliability of messages that are passed between people as common knowledge emerges. The proposal is developed on the background of our own empirical studies and outlines different ways common knowledge might be comprised. We discuss what these issues may tell us about ASD, about the relation between social and non-social cognition, about social objects, and about the dynamics of social networks. (shrink)

Philosophical discussions on causal inference in medicine are stuck in dyadic camps, each defending one kind of evidence or method rather than another as best support for causal hypotheses. Whereas Evidence Based Medicine advocates the use of Randomised Controlled Trials and systematic reviews of RCTs as gold standard, philosophers of science emphasise the importance of mechanisms and their distinctive informational contribution to causal inference and assessment. Some have suggested the adoption of a pluralistic approach to causal inference, and an inductive (...) rather than hypothetico-deductive inferential paradigm. However, these proposals deliver no clear guidelines about how such plurality of evidence sources should jointly justify hypotheses of causal associations. We here develop such guidelines by first giving a philosophical analysis of the underpinnings of Hill’s viewpoints on causality. We then put forward an evidence-amalgamation framework adopting a Bayesian net approach to model causal inference in pharmacology for the assessment of harms. Our framework accommodates a number of intuitions already expressed in the literature concerning the EBM vs. pluralist debate on causal inference, evidence hierarchies, causal holism, relevance, and reliability. (shrink)

I reconstruct the discovery of the Higgs boson by the ATLAS collaboration at CERN as the application of a series of inferences from effects to causes. I show to what extent such diagnostic causal inferences can be based on well established knowledge gained in previous experiments. To this extent, causal reasoning can be used to infer the existence of entities, rather than just causal relationships between them. The resulting account relies on the principle of causality, attributes only a heuristic role (...) to the theory’s predictions, and shows how, and to what extent, data selection can be used to exclude alternative causes, even “unconceived” ones. (shrink)

Using the flexibility of recently developed methods for causal discovery based on Boolean satisfiability solvers, we encode a variety of assumptions that weaken the Faithfulness assumption. The encoding results in a number of SAT-based algorithms whose asymptotic correctness relies on weaker conditions than are standardly assumed. This implementation of a whole set of assumptions in the same platform enables us to systematically explore the effect of weakening the Faithfulness assumption on causal discovery. An important effect, suggested by simulation results, is (...) that adopting weaker assumptions greatly alleviates the problem of conflicting constraints and substantially shortens solving time. As a result, SAT-based causal discovery is potentially more scalable under weaker assumptions. (shrink)

This work investigates the intersection property of conditional independence. It states that for random variables A,B,C and X we have that X⊥⊥A|B,C and X⊥⊥B|A,C implies X⊥⊥(A,B)|C. Here, “⊥⊥” stands for statistical independence. Under the assumption that the joint distribution has a density that is continuous in A,B and C, we provide necessary and sufficient conditions under which the intersection property holds. The result has direct applications to causal inference: it leads to strictly weaker conditions under which the graphical structure becomes (...) identifiable from the joint distribution of an additive noise model. (shrink)

In medicine and the social sciences, researchers must frequently integrate the findings of many observational studies, which measure overlapping collections of variables. For instance, learning how to prevent obesity requires combining studies that investigate obesity and diet with others that investigate obesity and exercise. Recently developed causal discovery algorithms provide techniques for integrating many studies, but little is known about what can be learned from such algorithms. This article argues that there are causal facts that one could learn by conducting (...) a large study but which could not be learned by combining many smaller studies. Moreover, I characterize the frequency with which combining many studies increases underdetermination and exactly how much information is lost. 1 Introduction2 Causal Inference from Observational Data3 Piecemeal Causal Inference4 The Extent and Frequency of the Problem of Piecemeal Induction5 Conclusion. (shrink)

The essential precondition of implementing interventionist techniques of causal reasoning is that particular variables are identified as so-called intervention variables. While the pertinent literature standardly brackets the question how this can be accomplished in concrete contexts of causal discovery, the first part of this paper shows that the interventionist nature of variables cannot, in principle, be established based only on an interventionist notion of causation. The second part then demonstrates that standard observational methods that draw on Bayesian networks identify intervention (...) variables only if they also answer the questions that can be answered by interventionist techniques—which are thus rendered dispensable. The paper concludes by suggesting a way of identifying intervention variables that allows for exploiting the whole inferential potential of interventionist techniques. (shrink)

Semmelweis’s discovery of the cause of puerperal fever around the middle of the 19th century counts among the paradigm cases of scientific discovery. For several decades, philosophers of science have used the episode to illustrate, appraise and compare views of proper scientific methodology.Here I argue that the episode can be profitably reexamined in light of two cognate notions: causal reasoning and mechanisms. Semmelweis used several causal reasoning strategies both to support his own and to reject competing hypotheses. However, these strategies (...) have gone unappreciated in the existing literature. I show that a causal reasoning approach makes sense of the multitude of tables in Semmelweis’s main text, which in later editions were often abridged because they appeared redundant.Moreover, the existing literature tends to focus on Semmelweis’s clinical intervention and on the extent to which it alone confirms his theoretical conclusions. This neglects Semmelweis’s efforts to show by animal experiments that his clinical results are in agreement with a demonstrable mechanism of puerperal fever pathogenesis. I argue that the full evidential force of Semmelweis’s argument can only be appreciated if both his clinical and his laboratory investigations are taken into account. (shrink)

This thesis presents an alternative method for the detection of MIMIC models. Previous methods (such as factor analysis) suffer from a number of significant aws and limitations, which the new method (a causal search algorithm) doesn't suffer. A new algorithm is introduced, followed by a worked-through example of its application. Discussion focuses on some of the limiting assumptions the algorithm currently requires. Finally, recommendations for future work address improvements of the algorithm, as well as its applicability.

In Hume’s own day, and for nearly two hundred years after that, readers interested in his account of causal reasoning tended to focus on the skeptical implications of that account. For example, in his 1757 View of the Principal Deistical Writers of the Last and Present Century, John Leland characterized Hume as “endeavouring to destroy all reasoning, from causes to effects, or from effects to causes.”1 According to this sort of reading, as Louis Loeb describes it, “there is equal justification (...) for every belief about the unobserved—none whatsoever.”2 However, a consensus has now emerged in the secondary literature that while Hume is clearly skeptical about whether beliefs formed through causal inference are .. (shrink)

Many of our decisions pertain to causal systems. Nevertheless, only recently has it been claimed that people use causal models when making judgments, decisions and predictions, and that causal Bayes nets allow us to formally describe these inferences. Experimental research has been limited to simple, artificial problems, which are unrepresentative of the complex dynamic systems we successfully deal with in everyday life. For instance, in social interactions, we can explain the actions of other's on the fly and we can generalize (...) from limited observations to predict future actions and their consequences. Our main argument is that none of these inferences (i.e., induction, generalization, explanation, and prediction) can be achieved without causal reasoning. As a case in point we use the popular television series desperate housewives and show how causal Bayes nets are able to explain the inferences made in social contexts. Crucially, causal Bayes nets also allow us to understand why we can infer so much from so little when making sense of a protagonist's behavior. (shrink)

In psychiatry, pharmacological drugs play an important experimental role in attempts to identify the neurobiological causes of mental disorders. Besides being developed in applied contexts as potential treatments for patients with mental disorders, pharmacological drugs play a crucial role in research contexts as experimental instruments that facilitate the formulation and revision of neurobiological theories of psychopathology. This paper examines the various epistemic functions that pharmacological drugs serve in the discovery, refinement, testing, and elaboration of neurobiological theories of mental disorders. I (...) articulate this thesis with reference to the history of antipsychotic drugs and the evolution of the dopamine hypothesis of schizophrenia in the second half of the twentieth century. I argue that interventions with psychiatric patients through the medium of antipsychotic drugs provide researchers with information and evidence about the neurobiological causes of schizophrenia. This analysis highlights the importance of pharmacological drugs as research tools in the generation of psychiatric knowledge and the dynamic relationship between practical and theoretical contexts in psychiatry. (shrink)

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

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

Causal reasoning is primarily concerned with what would happen to a system under external interventions. In particular, we are often interested in predicting the probability distribution of some random variables that would result if some other variables were forced to take certain values. One prominent approach to tackling this problem is based on causal Bayesian networks, using directed acyclic graphs as causal diagrams to relate post-intervention probabilities to pre-intervention probabilities that are estimable from observational data. However, such causal diagrams are (...) seldom fully testable given observational data. In consequence, many causal discovery algorithms based on data-mining can only output an equivalence class of causal diagrams. This paper is concerned with causal reasoning given an equivalence class of causal diagrams, represented by a ancestral graph. We present two main results. The first result extends Pearl 's celebrated do-calculus to the context of ancestral graphs. In the second result, we focus on a key component of Pearl's calculus---the property of invariance under interventions, and give stronger graphical conditions for this property than those implied by the first result. The second result also improves the earlier, similar results due to Spirtes et al. (shrink)

There has been an intense discussion, albeit largely an implicit one, concerning the inference of causal hypotheses from statistical correlations in quantum mechanics ever since John Bell’s first statement of his notorious theorem in 1966. As is well known, its focus has mainly been the so-called Einstein-Podolsky-Rosen (“EPR”) thought experiment, and the ensuing observed correlations in real EPR like experiments. But although implicitly the discussion goes as far back as Bell’s work, it is only in the last two decades that (...) it has become recognizably and explicitly a debate about causal inference in the quantum realm. The bulk of this paper is devoted to a review of three influential arguments in the philosophical literature that aim to show that causal models for the EPR correlations are impossible, due to Bas Van Fraassen, Daniel Hausman and Huw Price. I contend that all these arguments are inconclusive since they contain premises or presuppositions that are false, unwarranted, or at least controversial. Five different common cause models are outlined that seem perfectly viable for the EPR correlations. These models are then employed to illustrate various difficulties with the premises and presuppositions underlying Van Fraassen’s, Hausman’s and Price’s arguments. In all these cases it is argued that the difficulties cut deep against these authors’ own theories of causation and causal inference. My conclusions are that causal models for the EPR correlations remain viable, that philosophical work is still required to assess their relative virtues, and that in any case the mere theoretical conceivability and empirical possibility of these models sheds doubts over Van Fraassen’s, Hausman’s and (important elements in) Price’s theories of causation and causal inference. (shrink)

The literature on causal discovery has focused on interventions that involve randomly assigning values to a single variable. But such a randomized intervention is not the only possibility, nor is it always optimal. In some cases it is impossible or it would be unethical to perform such an intervention. We provide an account of ‘hard' and ‘soft' interventions and discuss what they can contribute to causal discovery. We also describe how the choice of the optimal intervention(s) depends heavily on the (...) particular experimental setup and the assumptions that can be made. ‡The first author is funded by the Causal Learning Collaborative Initiative supported by the James S. McDonnell Foundation. Many aspects of this paper were inspired by discussions with members of the collaborative. †To contact the authors, please write to: Department of Philosophy, Carnegie Mellon University, Pittsburgh, PA 15213; e-mail: [email protected] and [email protected]. (shrink)

Most causal discovery algorithms in the literature exploit an assumption usually referred to as the Causal Faithfulness or Stability Condition. In this paper, we highlight two components of the condition used in constraint-based algorithms, which we call “Adjacency-Faithfulness” and “Orientation- Faithfulness.” We point out that assuming Adjacency-Faithfulness is true, it is possible to test the validity of Orientation- Faithfulness. Motivated by this observation, we explore the consequence of making only the Adjacency-Faithfulness assumption. We show that the familiar PC algorithm has (...) to be modified to be correct under the weaker, Adjacency-Faithfulness assumption. The modified algorithm, called Conservative PC (CPC), checks whether Orientation- Faithfulness holds in the orientation phase, and if not, avoids drawing certain causal conclusions the PC algorithm would draw. Howtion: ever, if the stronger, standard causal Faith-. (shrink)

Transcranial magnetic stimulation (TMS) is a method capable of transiently modulating neural excitability. Depending on the stimulation parameters information processing in the brain can be either enhanced or disrupted. This way the contribution of different brain areas involved in mental processes can be studied, allowing a functional decomposition of cognitive behavior both in the temporal and spatial domain, hence providing a functional resolution of brain/mind processes. The aim of the present paper is to argue that TMS with its ability to (...) draw causal inferences on function and its neural representations is a valuable neurophysiological tool for investigating the causal basis of neuronal functions and can provide substantive insight into the modern interdisciplinary and (anti)reductionist neurophilosophical debates concerning the relationships between brain functions and mental abilities. Thus, TMS can serve as a heuristic method for resolving causal issues in an arena where only correlative tools have traditionally been available. (shrink)

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

A theory of categorization is presented in which knowledge of causal relationships between category features is represented in terms of asymmetric and probabilistic causal mechanisms. According to causal‐model theory, objects are classified as category members to the extent they are likely to have been generated or produced by those mechanisms. The empirical results confirmed that participants rated exemplars good category members to the extent their features manifested the expectations that causal knowledge induces, such as correlations between feature pairs that are (...) directly connected by causal relationships. These expectations also included sensitivity to higher‐order feature interactions that emerge from the asymmetries inherent in causal relationships. Quantitative fits of causal‐model theory were superior to those obtained with extensions to traditional similarity‐based models that represent causal knowledge either as higher‐order relational features or “prior exemplars” stored in memory. (shrink)

Critics of automatic causal discovery have claimed that Tetrad-style algorithms are inferior to domain experts at discovering causal structure from real scientific data and especially poor when applied to data that is highly mixed, either in a physical sense or in a mixtures of records sense. We compare a domain expert in geological spectroscopy head-to-head with a variety of machine algorithms on the task of predicting mineral class composition from visual to near infrared reflectance spectra. A simplified Tetrad algorithm outperforms (...) all other machine algorithms tested and performs comparably to the domain expert. We conclude: that Tetrad algorithms can perform as well as human experts on this task, and that mixtures do not necessarily undermine the reliability of Tetrad algorithms on this task. This constitutes a counter-examples to the claim that machine algorithms are necessarily inferior to domain experts on tasks involving causal reasoning with real scientific data. (shrink)

A cause may influence its effect via multiple paths. Paradigmatically (Hesslow [1974]), taking birth control pills both decreases one’s risk of thrombosis by preventing pregnancy and increases it by producing a blood chemical. Building on Pearl ([2001]), I explicate the notion of a path-specific effect. Roughly, a path-specific effect of C on E via path P is the degree to which a change in C would change E were they to be transmitted only via P. Facts about such effects may (...) be gleaned from the structural equations commonly used to represent the causal relationships among variables. I contrast my analysis of the Hesslow case with those given by theorists of probabilistic causality, who mistakenly link it to issues of causal heterogeneity, token-causation and indeterminism. The reason probabilistic theories misdiagnose this case is that they pay inadequate attention to the structural relationships among variables. (shrink)

Recent approaches to causal modelling rely upon the causal Markov condition, which specifies which probability distributions are compatible with a directed acyclic graph. Further principles are required in order to choose among the large number of DAGs compatible with a given probability distribution. Here we present a principle that we call frugality. This principle tells one to choose the DAG with the fewest causal arrows. We argue that frugality has several desirable properties compared to the other principles that have been (...) suggested, including the well-known causal faithfulness condition. _1_ Introduction _2_ The Causal Markov Condition _3_ Faithfulness _4_ Frugality _4.1_ Basic independences and frugality _4.2_ General properties of directed acyclic graphs satisfying frugality _4.3_ Connection to minimality assumptions _5_ Frugality as a Parsimony Principle _6_ Conclusion Appendix. (shrink)

How do we determine whether some candidate causal factor is an actual cause of some particular outcome? Many philosophers have wanted a view of actual causation which fits with folk intuitions of actual causation and those who wish to depart from folk intuitions of actual causation are often charged with the task of providing a plausible account of just how and where the folk have gone wrong. In this paper, I provide a range of empirical evidence aimed at showing just (...) how and where the folk go wrong in determining whether an actual causal relation obtains. The evidence suggests that folk intuitions of actual causation are generated by two epistemically defective processes. I situate the empirical evidence within a background discussion of debunking, arguing for a two-pronged debunking explanation of folk intuitions of actual causation. I conclude that those who wish to depart from folk intuitions of actual causation should not be compelled to square their account of actual causation with the verdicts of the folk. In the dispute over actual causation, folk intuitions deserve to be rejected. (shrink)

For much of the twentieth century, philosophy and science went their separate ways. In moral philosophy, fear of the so-called naturalistic fallacy kept moral philosophers from incorporating developments in biology and psychology. Since the 1990s, however, many philosophers have drawn on recent advances in cognitive psychology, brain science, and evolutionary psychology to inform their work. This collaborative trend is especially strong in moral philosophy, and these three volumes bring together some of the most innovative work by both philosophers and psychologists (...) in this emerging interdisciplinary field. (shrink)

It is now common to hold that causes do not always determine their effects, and indeed theories of "probabilistic causation" abound. The basic idea of these theories is that C causes E just in case C and E both occur, and the chance of E would have been lower than it is had C not occurred. The problems with these accounts are that the notion of chance remains primitive, and this account of causation does not coincide with the intuitive notion (...) of causation as ontological support. ;Turning things around, I offer an analysis of chance in terms of causation, called the causal theory of chance. The chance of an event E is the degree to which it is determined by its causes. The chance of an event have full causal pedigrees, just like determined events; they are not "events from nowhere". I hold that, for stochastic as well as for deterministic processes, the actual history of a system is caused by its dynamical properties and the boundary condition. A system is stochastic if the actual history is not fully determined by maximal knowledge of these causes, i.e. it is not logically entailed by them. ;If chance involves partial determination, and determination is logical entailment, then there must be such a thing as partial entailment, or logical probability. To make the notion of logical probability plausible, in the face of current opposition to it, I offer a new account of logical probability which meets objections levelled at the previous accounts of Keynes and Carnap. ;The causal theory of chance, unlike its competitors, satisfies all of the following criteria: Chance is defined for single events. Chance supervenes on the physical properties of the system in question. Chance is a probability function, i.e. a normalised measure. Knowledge of the chance of an event warrants a numerically equal degree of belief, i.e. Miller's Principle can be derived within the theory. Chance is empirically accessible, within any given range of error, by measuring relative frequencies. With an additional assumption, the theory entails Reichenbach's Common Cause Principle . The theory enables us to make sense of probabilities in quantum mechanics. ;The assumption used to prove the CCP is that the state of a system represents complete information, so that the state of a composite system "factorises" into a logical conjunction of states for the sub-systems. To make sense of quantum mechanics, particularly the EPR experiment, we drop this assumption. In this case, the EPR criterion of reality is false. It states that if an event E is predictable, and locally caused, then it is locally predictable. This fails when maximal information about a pair of systems does not factorise, leading to a non-locality of knowledge. (shrink)

Inductive reasoning is fundamental to human cognition, yet it remains unclear how we develop this ability and what might influence our inductive choices. We created novel categories in which crucial factors such as domain and category structure were manipulated orthogonally. We trained 403 4–9-year-old children to categorise well-matched natural kind and artefact stimuli with either featural or relational category structure, followed by induction tasks. This wide age range allowed for the first full exploration of the developmental trajectory of inductive reasoning (...) in both domains. We found a gradual transition from perceptual to categorical induction with age. This pattern was stable across domains, but interestingly, children showed a category bias one year later for relational categories. We hypothesise that the ability to use category information in inductive reasoning develops gradually, but is delayed when children need to process and apply more complex category structures. (shrink)

Tight correlations between mental states and brain states have been observed time and again within the ethology of biologically ingrained animal behaviors, the comparative psychology of animal minds, the evolutionary psychology of mental adaptations, the behavioral genetics of inherited mental traits, the developmental psychology of the maturing mind, the psychopharmacology of mind-altering substances, and cognitive neuroscience more generally. They imply that our mental lives are only made possible because of brain activity—that having a functioning brain is a necessary condition for (...) having conscious experiences. This mind-brain dependence thesis yields predictions that have been spectacularly confirmed by the evidence, whereas its antithesis has been massively disconfirmed—at least so long as it is not formulated so vaguely as to yield no specific empirical consequences at all. Dualists are thus forced to make a difficult choice: either retain a belief in personal survival at the expense of ignoring or dismissing the implications of our best evidence, or accept those implications at the expense of acknowledging that the prospects for personal survival are extremely dim. -/- 1. Introduction: A Probabilistic Argument -- 2. Scientific Reasoning I: Mill’s Methods and Modern Epidemiological Principles - 2.1 When Correlation Indicates Causation -- 3. Scientific Reasoning II: Inference to the Best Explanation, Testability, and Predictive Success - 3.1 Parsimony: Tempering Explanations to Minimize False Beliefs - 3.2 Plausibility: Harmonizing Explanations with Background Knowledge - 3.3 Testability and Confirmation: Checking Hypotheses Against the Data - 3.3.1 Confirmation of the Dependence Thesis from Failed Attempts to Falsify It - 3.3.2 Confirmation of the Dependence Thesis from the Falsification of its Rival - 3.4 Scope: Bringing Disparate Facts Together - 3.5 Fertility: Generating New Lines of Research - 3.6 Overview -- 4. Scientific Reasoning III: Bayesian and Information-Theoretic Approaches - 4.1 Bayesian Probability - 4.2 Bayesian Confirmation Theory - 4.3 A Bayesian Approach to the Dependence and Independence Theses - 4.4 The Probabilistic Price of Ad Hoc Accommodation: Simplicity vs. Goodness of Fit - 4.4.1 Simplicity Reflected in the Likelihood - 4.4.2 Simplicity Reflected in the Prior Probability - 4.5 Prediction vs. Accommodation - 4.6 Bayesian Analysis of the Dependence and Independence Theses -- 5. The Dualist’s Dilemma: Reject Science, or Reject Personal Survival? - 5.1 What’s Left for a Soul to Do? -- 6. Conclusion. (shrink)

A medical intervention is a medical procedure or application intended to relieve or prevent illness or injury. Examples of medical interventions include vaccination and drug administration. After a medical intervention, adverse events (AEs) may occur which lie outside the intended consequences of the intervention. The representation and analysis of AEs are critical to the improvement of public health. Description: The Ontology of Adverse Events (OAE), previously named Adverse Event Ontology (AEO), is a community-driven ontology developed to standardize and integrate data (...) relating to AEs arising subsequent to medical interventions, as well as to support computer-assisted reasoning. OAE has over 3,000 terms with unique identifiers, including terms imported from existing ontologies and more than 1,800 OAE-specific terms. In OAE, the term ‘adverse event’ denotes a pathological bodily process in a patient that occurs after a medical intervention. Causal adverse events are defined by OAE as those events that are causal consequences of a medical intervention. OAE represents various adverse events based on patient anatomic regions and clinical outcomes, including symptoms, signs, and abnormal processes. OAE has been used in the analysis of several different sorts of vaccine and drug adverse event data. (shrink)

Data analysis that merely fits an empirical covariance matrix or that finds the best least squares linear estimator of a variable is not of itself a reliable guide to judgements about policy, which inevitably involve causal conclusions. The policy implications of empirical data can be completely reversed by alternative hypotheses about the causal relations of variables, and the estimates of a particular causal influence can be radically altered by changes in the assumptions made about other dependencies.2 For these reasons, one (...) of the common aims of empirical research in the.. (shrink)

The anti-causal prophecies of last century have been disproved. Causality is neither a ‘relic of a bygone’ nor ‘another fetish of modern science’; it still occupies a large part of the current debate in philosophy and the sciences. This investigation into causal modelling presents the rationale of causality, i.e. the notion that guides causal reasoning in causal modelling. It is argued that causal models are regimented by a rationale of variation, nor of regularity neither invariance, thus breaking down the dominant (...) Human paradigm. The notion of variation is shown to be embedded in the scheme of reasoning behind various causal models: e.g. Rubin’s model, contingency tables, and multilevel analysis. It is also shown to be latent – yet fundamental – in many philosophical accounts. Moreover, it has significant consequences for methodological issues: the warranty of the causal interpretation of causal models, the levels of causation, the characterisation of mechanisms, and the interpretation of probability. This book offers a novel philosophical and methodological approach to causal reasoning in causal modelling and provides the reader with the tools to be up to date about various issues causality rises in social science. "Dr. Federica... more on http://springer.com/978-1-4020-8816-2.. (shrink)

Previous studies of children’s counterfactual reasoning have focused on scenarios in which a single causal event yielded an outcome. However, there are also cases in which an outcome would have occurred even in the absence of its actual cause, because of the presence of a further potential cause. In this study, 152 children aged 4-9 years reasoned counterfactually about such scenarios, in which there were ‘doubly-determined’ outcomes. The task involved dropping two metal discs down separate runways, each of which was (...) sufficient to knock over a toy pig. One of the runways was shorter than the other, meaning that one of the discs actually knocked over the pig whereas the other always arrived too late to do so. Children were asked whether the pig would have been knocked over in the absence of the first metal disc descending the runway. We found that children could accurately answer such counterfactual questions by 6-7 years. (shrink)

This paper, like its companion explores some ways in which, on the one hand, normative theorizing about causation and causal reasoning and, on the other, empirical psychological investigations into causal cognition can be mutually illuminating. The paper carries out this exploration in connection with a variety of topics—the role of information about the presence of a “physical connection” between cause and effect in causal judgment, the role of “proportionality” in choosing the appropriate “level” of explanation, and the role of mechanism (...) information in causal judgment. (shrink)

This paper looks at progressive neurological diseases showing brain decline correlated with the decline of consciousness, as well as the content of consciousness. For instance, a young man’s healthy and fully functional brain generated an intelligent and lovely self, but then an aggressive brain tumor grew deep within his brain. As the tumor grew, it rendered brain tissue dysfunction and direct effects followed. From focal destruction of brain tissue, an aphasia first results. What follows is the dissolution of a functional (...) brain, a mind, a person, and what some call a soul. From more widespread destruction of brain tissue, more functions erode until a minimally conscious state results. Intact and functional brain tissue is required to produce one’s consciousness and personality. When these brain tissues become dysfunctional and die, everything taken as the soul appears to die with them. -/- 1. Left Brain Slow Progressive Decline - 1.1 Behind the Case -- 2. Right Brain Progressive Decline -- 3. Alien Limb -- 4. Fallacious Objections -- 5. Lucid Moments, Coma Recoveries -- 6. The Correlation is Not Causation “Problem”. (shrink)

In this book, Reiss argues in favor of a tight fit between evidence, concept and purpose in our causal investigations in the sciences. There is no doubt that the sciences employ a vast array of techniques to address causal questions such as controlled experiments, randomized trials, statistical and econometric tools, causal modeling and thought experiments. But how do these different methods relate to each other and to the causal inquiry at hand? Reiss argues that there is no "gold standard" in (...) settling causal issues against which other methods can be measured. Rather, the various methods of inference tend to be good only relative to certain interpretations of the word "cause", and each interpretation, in turn, helps to address some salient purpose but not others. The main objective of this book is to explore the metaphysical and methodological consequences of this view in the context of numerous cases studies from the natural and social sciences. (shrink)

Studies of primate cognition have conclusively shown that humans and apes share a range of basic cognitive abilities. As a corollary, these same studies have also focussed attention on what makes humans unique, and on when and how specifically human cognitive skills evolved. There is widespread agreement that a major distinguishing feature of the human mind is its capacity for causal reasoning. This paper argues that causal cognition originated with the use made of indirect natural signs by early hominins forced (...) to adapt to variable late Miocene and early Pliocene environments; that early hominins evolved an innate tendency to search for such signs and infer their causes; that causal inference required the existence of incipient working memory; and that causal relationships were stored through being integrated into spatial maps to create increasingly complex causal models of the world. (shrink)

This paper, like its companion explores some ways in which, on the one hand, normative theorizing about causation and causal reasoning and, on the other, empirical psychological investigations into causal cognition can be mutually illuminating. The topics considered include the connection between causal claims and claims about the outcomes of interventions and the various ways that invariance claims figure in causal judgment.

Causal inference plays a central role in behavioral science. Historically, behavioral science methodologies have typically sought to infer a single causal relation. Each of the major approaches to causal inference in the behavioral sciences follows this pattern. Nonetheless, such approaches sometimes differ in the causal relation that they infer. Incremental causal inference offers an alternative to this conceptualization of causal inference that divides the inference into a series of incremental steps. Different steps infer different causal relations. Incremental causal inference is (...) consistent with both causal pluralism and anti-pluralism. However, anti-pluralism places greater constraints the possible topology of sequential inferences. Arguments against causal inference include questioning consistency with causation as an explanatory principle, charging undue complexity, and questioning the need for it. Arguments in favor of incremental inference include better explanation of diverse causal inferences in behavioral science, tailored causal inference, and more detailed and explicit description of causal inference. Incremental causal inference offers a viable and potentially fruitful alternative to approaches limited to a single causal relation. (shrink)