Causal Modeling

Alexander Gebharter has recently proposed to use Bayesian network causal discovery methods to identify the constitutive dependencies that underwrite mechanistic explanations. The proposal depends on using the assumptions of the causal Bayesian network framework to implicitly define mechanistic constitution as a kind of deterministic direct causal dependence. The aim of this paper is twofold. In the first half, we argue that Gebharter’s proposal incurs severe conceptual problems. In the second half, we present an alternative way to bring Bayesian network tools (...) to bear on the issue of understanding mechanistic constitution. More precisely, our proposal interprets constitution as the relation explaining why a target phenomenon has its characteristic causal role in terms of the causal roles of some of its spatiotemporal parts---where the notion of causal role is probabilistically understood. (shrink)

The paper investigates the kind of dependence relation that best portrays Machian frame-dragging in general relativity. The question is tricky because frame-dragging relates local inertial frames to distant distributions of matter in a time-independent way, thus establishing some sort of non-local link between the two. For this reason, a plain causal interpretation of frame-dragging faces huge challenges. The paper will shed light on the issue by using a generalized structural equation model analysis in terms of manipulationist counterfactuals recently applied in (...) the context of metaphysical enquiry by Schaffer (2016) and Wilson (2017). The verdict of the analysis will be that frame-dragging is best understood in terms of a novel type of dependence relation that is half-way between causation and grounding. (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)

An important problem in many domains is to predict how a system will respond to interventions. This task is inherently linked to estimating the system’s underlying causal structure. To this end, Invariant Causal Prediction [1] has been proposed which learns a causal model exploiting the invariance of causal relations using data from different environments. When considering linear models, the implementation of ICP is relatively straightforward. However, the nonlinear case is more challenging due to the difficulty of performing nonparametric tests for (...) conditional independence. In this work, we present and evaluate an array of methods for nonlinear and nonparametric versions of ICP for learning the causal parents of given target variables. We find that an approach which first fits a nonlinear model with data pooled over all environments and then tests for differences between the residual distributions across environments is quite robust across a large variety of simulation settings. We call this procedure “invariant residual distribution test”. In general, we observe that the performance of all approaches is critically dependent on the true causal structure and it becomes challenging to achieve high power if the parental set includes more than two variables. As a real-world example, we consider fertility rate modeling which is central to world population projections. We explore predicting the effect of hypothetical interventions using the accepted models from nonlinear ICP. The results reaffirm the previously observed central causal role of child mortality rates. (shrink)

Mitigating response distortion in answers to sensitive questions is an important issue for business ethics researchers. Sensitive questions may be asked in surveys related to business ethics, and respondents may intend to avoid exposing sensitive aspects of their character by answering such questions dishonestly, resulting in response distortion. Previous studies have provided evidence that a surveying procedure called the randomized response technique is useful for mitigating such distortion. However, previous studies have mainly applied the RRT to individual dichotomous questions in (...) face-to-face survey settings. In this study, we focus on behavioral research examining the relationships between latent variables, which are unobserved variables measured by multiple items on Likert or bipolar scales. To demonstrate how the RRT can be applied to obtain valid answers from respondents answering a self-administered online questionnaire with Likert and bipolar scales, we build a behavioral model to study the effect of punishment severity on employees’ attitudes toward misuse of information systems resources in the workplace, which in turn influence misuse behavior. The survey findings meet our expectations. The respondents are generally more willing to disclose sensitive data about their attitudes and actual behavior related to misuse when the RRT is implemented. The RRT’s implications for causal modeling and the advantages and challenges of its use in online environments are also discussed. (shrink)

The definition of metabolic syndrome has been, and still is, extremely controversial. My purpose is not to give a solution to the associated debate but to argue that the controversy is at least partially due to the different ‘causal content’ of the various definitions: their theoretical validity and practical utility can be evaluated by reconstructing or making explicit the underlying causal structure. I will therefore propose to distinguish the alternative definitions according to the kinds of causal content they carry: definitions (...) grounded on associations, definitions presupposing a causal model built upon statistical associations, and definitions grounded on underlying mechanisms. I suggest that analysing definitions according to their causal content can be helpful in evaluating alternative definitions of some diseases. I want to show how the controversy over MetS suggests a distinction among three kinds of definitions based on how explicitly they characterise the syndrome in causal terms, and on the type of causality involved. I will call ‘type 1 definitions’ those definitions that are purely associative; ‘type 2 definitions’ the definitions based on statistical associations, plus generic medical and causal knowledge; and ‘type 3 definitions’ the definitions based on mechanisms. These kinds of definitions, although different, can be related to each other. A definition with more specific causal content may be useful in the evaluation of definitions characterised by a lower degree of causal specificity. Moreover, the identification of the type of causality involved is of help to constitute a good criterion for choosing among different definitions of a pathological entity. In section I introduce the controversy about MetS, in section I propose some remarks about medical definitions and their ‘causal import’, and in section I suggest that the different attitudes towards the definition of MetS are relevant to evaluate their explicative power. (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)

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

According to Woodward’s causal model of explanation, explanatory information is relevant for manipulation purposes and indicates by means of invariant causal relations how to change the value of certain target explanandum variables by intervening on others. Therefore, the depth of an explanation is evaluated through the size of the domain of invariance of the generalization involved. In this article, I argue that Woodward’s account of explanatory relevance is still unsatisfactory and claim that the depth of an explanation should be explicated (...) in terms of the size of the domain of circumstances which it designates as leaving the explanandum unchanged. (shrink)

In Woodward's causal model of explanation, explanatory information is information that is relevant to manipulation and control and that affords to change the value of some target explanandum variable by intervening on some other. Accordingly, the depth of an explanation is evaluated through the size of the domain of invariance of the generalization involved. In this paper, I argue that Woodward's treatment of explanatory relevance in terms of invariant causal relations is still wanting and suggest to evaluate the depth of (...) an explanation through the size of the domain of circumstances that it designates as leaving the explanandum unchanged. (shrink)

This paper develops the view that in arguing informally individuals construct a dual representation in which there is a coupling of arguments and the structure of the qualitative (mental) causal model to which these refer. Invited to consider a future possibility, individuals generate a causal model and mentally simulate the consequences of certain actions. Their arguments refer to the causal paths in the model. Correspondingly, faced with specific arguments about a policy option they generate a model with particular causal paths (...) and mentally simulate the outcomes. The results of Experiment 1 are consistent with this notion. Decisions on the percentage of funds to be allocated to genetically modified (GM) crop research depended on the structure of the arguments elicited in response to imagining a future state of affairs. Specifically, the presence of a dissuasive argument eliminated the impact of any persuasive argument. The non-monotonic properties of everyday informal argument can then be seen as a corollary of change to causal structure in the model. The dual representation view predicts that the impact of a dissuasive argument will depend on the structure of the causal model. Experiment 2 tested and confirmed this prediction by requiring individuals to judge the relative persuasiveness of two cases referring either to a model with two independent causal paths or to a model in which one causal path depended on the other. In contrast to Experiment 1, prior opinion on GM crop research did not affect allocation decisions. An advisory role in contrast to a participant role may encourage a more decontextualised mode of thinking. According to the dual representation view, ease of mental simulation should exert wide-ranging effects on judgements and the rhetoric of arguments should also be important. The paper concludes with a discussion of some of these expectations. (shrink)

A survey of the modern theories of explanation leads us to conclude that it is J. Woodward\'s conterfactual approach that seems to set out a promising agenda for setting up a satisfactory model of explanation. Such a model would need to come to terms with two stipulations, i.e. to reflect causal asymmetry and be empirically adequate. To meet the latter I modify the counterfactual model of explanation to require a class of observationally equivalent (Bayesian) causal models instead of the true (...) causal model. For in most cases empirical adequacy of causal models, i.e. their identifiability and consistency with observed distributions, would neither grant the stronger conclusion Woodward embeds in his conterfactual account of explanation nor license metaphysical naturalism. (shrink)

It has recently been argued that successful evidence-based policy should rely on two kinds of evidence: statistical and mechanistic. The former is held to be evidence that a policy brings about the desired outcome, and the latter concerns how it does so. Although agreeing with the spirit of this proposal, we argue that the underlying conception of mechanistic evidence as evidence that is different in kind from correlational, difference-making or statistical evidence, does not correctly capture the role that information about (...) mechanisms should play in evidence-based policy. We offer an alternative account of mechanistic evidence as information concerning the causal pathway connecting the policy intervention to its outcome. Not only can this be analyzed as evidence of difference-making, it is also to be found at any level and is obtainable by a broad range of methods, both experimental and observational. Using behavioral policy as an illustration, we draw the implications of this revised understanding of mechanistic evidence for debates concerning policy extrapolation, evidence hierarchies, and evidence integration. (shrink)

The present paper aims to complement causal model approaches to causal explanation by Woodward [15], Halpern and Pearl [5], and Strevens [14]. It centres on a strengthened Ramsey Test of conditionals: α ≫ γ iff, after sus- pending judgment about α and γ, an agent can infer γ from the supposition of α. It has been shown by Andreas and Gu ̈nther [1] that such a conditional can be used as starting point of an analysis of ‘because’ in natural language. (...) In what follows, we shall refine this analysis so as to yield a fully fledged account of causal explanation. (shrink)

Evidential reasoning is hard, and errors can lead to miscarriages of justice with serious consequences. Analytic methods for the correct handling of evidence come in different styles, typically focusing on one of three tools: arguments, scenarios or probabilities. Recent research used Bayesian networks for connecting arguments, scenarios, and probabilities. Well-known issues with Bayesian networks were encountered: More numbers are needed than are available, and there is a risk of misinterpretation of the graph underlying the Bayesian network, for instance as a (...) causal model. The formalism presented here models presumptive arguments about coherent hypotheses that are compared in terms of their strength. No choice is needed between qualitative or quantitative analytic styles, since the formalism can be interpreted with and without numbers. The formalism is applied to key concepts in argumentative, scenario and probabilistic analyses of evidential reasoning, and is illustrated with a fictional crime investigation example based on Alfred Hitchcock’s film ‘To Catch A Thief’. (shrink)

The explanatory role of natural selection is one of the long-term debates in evolutionary biology. Nevertheless, the consensus has been slippery because conceptual confusions and the absence of a unified, formal causal model that integrates different explanatory scopes of natural selection. In this study we attempt to examine two questions: (i) What can the theory of natural selection explain? and (ii) Is there a causal or explanatory model that integrates all natural selection explananda? For the first question, we argue that (...) five explananda have been assigned to the theory of natural selection and that four of them may be actually considered explananda of natural selection. For the second question, we claim that a probabilistic conception of causality and the statistical relevance concept of explanation are both good models for understanding the explanatory role of natural selection. We review the biological and philosophical disputes about the explanatory role of natural selection and formalize some explananda in probabilistic terms using classical results from population genetics. Most of these explananda have been discussed in philosophical terms but some of them have been mixed up and confused. We analyze and set the limits of these problems. (shrink)

Notwithstanding the many strengths of the dynamic developmental theory, there remain challenges to be overcome before it can be incorporated into a true causal model of attention-deficit/hyperactivity disorder (ADHD). These include the development of reliable measures of reinforcement delay gradients, the validation of shortened reinforcement delay as an endophenotype, and the integration of this pathway with other potential pathways.

A realist causal model of quantum cosmology (QC) is developed. By applying the de Broglie-Bohm interpretation of quantum mechanics to QC, we resolve the notorious 'problem of time' in QC, and derive exact equations of motion for cosmological dynamical variables. Due to this success, it is argued that if the situation in QC is used as a yardstick by which other interpretations are measured, the de Broglie-Bohm theory seems uniquely fit as an interpretation of quantum mechanics.

Cartwright (1999a, 1999b) attacked the view that causal relations conform to the Markov condition by providing a counterexample in which a common cause does not screen off its effects: the prominent chemical factory. In this paper we suggest a new way to handle counterexamples to Markov causation such as the chemical factory. We argue that Cartwright’s as well as similar scenarios (such as decay processes, EPR/B experiments, or spontaneous macro breaking processes) feature a certain kind of non-causal dependence that kicks (...) in once the common cause occurs. We then develop a representation of this specific kind of non-causal dependence that allows for modeling the problematic scenarios in such a way that the Markov condition is not violated anymore. (shrink)

A counterlegal is a counterfactual conditional containing an antecedent that is inconsistent with some set of laws. A backtracker is a counterfactual that tells us how things would be at a time earlier than that of its antecedent, were the antecedent to obtain. Typically, theories that evaluate counterlegals appropriately don’t evaluate backtrackers properly, and vice versa. Two cases in point: Lewis’ ordering semantics handles counterlegals well but not backtrackers. Hiddleston’s :632–657, 2005) causal-model semantics nicely handles backtrackers but not counterlegals. Taking (...) Hiddleston’s account as a starting point, I offer steps toward a theory capable of handling both counterlegals and backtrackers. The core contribution of this paper is a means for evaluating counterlegals relative to minimally-illegal models. (shrink)

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

Companies' perceptions of corporate social responsibility have been only partially analyzed from an individual perspective that focuses on personal characteristics and professional backgrounds. However, a gap exists in the research on manager leadership styles and CSR perceptions from a gender perspective. Therefore, this article analyzes differences in attitudes toward various dimensions of CSR by focusing on the leadership styles—transformational, dominance, and dual perspectives—of male and female managers in Spain. A total of 391 respondents in top management positions in Spain were (...) surveyed. The findings revealed similarities and differences between genders with respect to leadership styles and CSR perceptions by dimension using a univariate analysis. A causal model that employed structural equation modeling was also estimated. The findings suggest that for transformational and dual leadership styles, Spanish women may be more adaptable and effective at pursuing company sustainability than Spanish men. However, dominance leadership was found to be the worst leadership style for deploying a CSR strategy. A number of conclusions for business management can be drawn, and some directions for future research are provided. (shrink)

Judea Pearl has argued that counterfactuals and causality are central to intelligence, whether natural or artificial, and has helped create a rich mathematical and computational framework for formally analyzing causality. Here, we draw out connections between these notions and various current issues in cognitive science, including the nature of mental “programs” and mental representation. We argue that programs (consisting of algorithms and data structures) have a causal (counterfactual-supporting) structure; these counterfactuals can reveal the nature of mental representations. Programs can also (...) provide a causal model of the external world. Such models are, we suggest, ubiquitous in perception, cognition, and language processing. (shrink)

In an influential article published in 1982, Bas Van Fraassen developed an argument against causal realism on the basis of an analysis of the Einstein-Podolsky-Rosen correlations of quantum mechanics. Several philosophers of science and experts in causal inference -including some causal realists like Wesley Salmon- have accepted Van Fraassen’s argument, interpreting it as a proof that the quantum correlations cannot be given any causal model. In this paper I argue that Van Fraassen’s article can also be interpreted as a good (...) guide to the different causal models available for the EPR correlations, and their relative virtues. These models in turn give us insight into some of the unusual features that quantum propensicies might have. (shrink)

Following the September 2001 terrorist attacks on the United States, much support for torture interrogation of terrorists has emerged in the public forum, largely based on the “ticking bomb” scenario. Although deontological and virtue ethics provide incisive arguments against torture, they do not speak directly to scientists and government officials responsible for national security in a utilitarian framework. Drawing from criminology, organizational theory, social psychology, the historical record, and my interviews with military professionals, I assess the potential of an official (...) U.S. program of torture interrogation from a practical perspective. The central element of program design is a sound causal model relating input to output. I explore three principal models of how torture interrogation leads to truth: the animal instinct model, the cognitive failure model, and the data processing model. These models show why torture interrogation fails overall as a counterterrorist tactic. They also expose the processes that lead from a precision torture interrogation program to breakdowns in key institutions—health care, biomedical research, police, judiciary, and military. The breakdowns evolve from institutional dynamics that are independent of the original moral rationale. The counterargument, of course, is that in a society destroyed by terrorism there will be nothing to repair. That is why the actual causal mechanism of torture interrogation in curtailing terrorism must be elucidated by utilitarians rather than presumed. (shrink)

One important aspect of biological explanation is detailed causal modeling of particular phenomena in limited experimental background conditions. Recognising this allows a new avenue for intertheoretic reduction to be seen. Reductions in biology are possible, when one fully recognises that a sufficient condition for a reduction in biology is a molecular model of 1) only the demonstrated causal parameters of a biological model and 2) only within a replicable experimental background. These intertheoretic identifications –which are ubiquitous in biology and form (...) the basis of ruthless reductions (Bickle 2003)- are criticised as merely “local” (Sullivan 2009) or “fragmentary” (Schaffner 2006). However, in an instructive case, a biological model is preserved in molecular terms, and a complex biological phenomenon has been successfully reduced. In doing this the molecular model remains valid in a broader range of background conditions and meaningfully unites disparate biological phenomena. (shrink)

Mitigating response distortion in answers to sensitive questions is an important issue for business ethics researchers. Sensitive questions may be asked in surveys related to business ethics, and respondents may intend to avoid exposing sensitive aspects of their character by answering such questions dishonestly, resulting in response distortion. Previous studies have provided evidence that a surveying procedure called the randomized response technique is useful for mitigating such distortion. However, previous studies have mainly applied the RRT to individual dichotomous questions in (...) face-to-face survey settings. In this study, we focus on behavioral research examining the relationships between latent variables, which are unobserved variables measured by multiple items on Likert or bipolar scales. To demonstrate how the RRT can be applied to obtain valid answers from respondents answering a self-administered online questionnaire with Likert and bipolar scales, we build a behavioral model to study the effect of punishment severity on employees’ attitudes toward misuse of information systems resources in the workplace, which in turn influence misuse behavior. The survey findings meet our expectations. The respondents are generally more willing to disclose sensitive data about their attitudes and actual behavior related to misuse when the RRT is implemented. The RRT’s implications for causal modeling and the advantages and challenges of its use in online environments are also discussed. (shrink)

Social research, from economics to demography and epidemiology, makes extensive use of statistical models in order to establish causal relations. The question arises as to what guarantees the causal interpretation of such models. In this paper we focus on econometrics and advance the view that causal models are ‘augmented’ statistical models that incorporate important causal information which contributes to their causal interpretation. The primary objective of this paper is to argue that causal claims are established on the basis of a (...) plurality of evidence. We discuss the consequences of ‘evidential pluralism’ in the context of econometric modelling. (shrink)

In this book, Reiss argues in favour 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 (prediction, explanation or policy analysis) 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)

We examine a formal semantics for counterfactual conditionals due to Judea Pearl, which formalizes the interventionist interpretation of counterfactuals central to the interventionist accounts of causation and explanation. We show that a characteristic principle validated by Pearl’s semantics, known as the principle of reversibility, states a kind of irreversibility: counterfactual dependence (in David Lewis’s sense) between two distinct events is irreversible. Moreover, we show that Pearl’s semantics rules out only mutual counterfactual dependence, not cyclic dependence in general. This, we argue, (...) suggests that Pearl’s logic is either too weak or too strong. (shrink)

A standard way of representing causation is with neuron diagrams. This has become popular since the influential work of David Lewis. But it should not be assumed that such representations are metaphysically neutral and amenable to any theory of causation. On the contrary, this way of representing causation already makes several Humean assumptions about what causation is, and which suit Lewis’s programme of Humean Supervenience. An alternative of a vector diagram is better suited for a powers ontology. Causation should be (...) understood as connecting property types and tokens where there are dispositions towards some properties rather than others. Such a model illustrates how an effect is typically polygenous: caused by many powers acting with each other, and sometimes against each other. It models causation as a tendency towards an effect which can be counteracted. The model can represent cases of causal complexity, interference, over-determination and causation of absence (equilibrium). (shrink)

Background -/- Previous literature suggests that those with reading disability (RD) have more pronounced deficits during semantic processing in reading as compared to listening comprehension. This discrepancy has been supported by recent neuroimaging studies showing abnormal activity in RD during semantic processing in the visual but not in the auditory modality. Whether effective connectivity between brain regions in RD could also show this pattern of discrepancy has not been investigated. Methodology/Principal Findings -/- Children (8- to 14-year-olds) were given a semantic (...) task in the visual and auditory modality that required an association judgment as to whether two sequentially presented words were associated. Effective connectivity was investigated using Dynamic Causal Modeling (DCM) on functional magnetic resonance imaging (fMRI) data. Bayesian Model Selection (BMS) was used separately for each modality to find a winning family of DCM models separately for typically developing (TD) and RD children. BMS yielded the same winning family with modulatory effects on bottom-up connections from the input regions to middle temporal gyrus (MTG) and inferior frontal gyrus(IFG) with inconclusive evidence regarding top-down modulations. Bayesian Model Averaging (BMA) was thus conducted across models in this winning family and compared across groups. The bottom-up effect from the fusiform gyrus (FG) to MTG rather than the top-down effect from IFG to MTG was stronger in TD compared to RD for the visual modality. The stronger bottom-up influence in TD was only evident for related word pairs but not for unrelated pairs. No group differences were noted in the auditory modality. Conclusions/Significance -/- This study revealed a modality-specific deficit for children with RD in bottom-up effective connectivity from orthographic to semantic processing regions. There were no group differences in connectivity from frontal regions, suggesting that the core deficit in RD is not in top-down modulation. (shrink)

Deborah Mayo's view of science is that learning occurs by severely testing specific hypotheses. Mayo expounded this thesis in her (1996) Error and the Growth of Experimental Knowledge (EGEK). This volume consists of a series of exchanges between Mayo and distinguished philosophers representing competing views of the philosophy of science. The tone of the exchanges is lively, edifying and enjoyable. Mayo's error-statistical philosophy of science is critiqued in the light of positions which place more emphasis on large-scale theories. The result (...) clarifies Mayo's account and highlights her contribution to the philosophy of science -- in particular, her contribution to the philosophy of those sciences that rely heavily on statistical analysis. The second half of the volume considers the application (or extension) of an error-statistical philosophy of science to theory testing in economics, causal modelling and legal epistemology. The volume also includes a contribution to the frequentist philosophy of statistics written by Mayo in collaboration with Sir David Cox. (shrink)

A path model of organizational creativity was presented; it conceptualized the influences of information sharing, learning culture, motivation, and networking on creative climate. A structural equation model was fitted to data from the pharmaceutical industry to test the proposed model. The model accounted for 86% of the variance in the creative climate dependent variable. Information sharing had a positive effect on learning culture, which in turn had a positive effect on creative climate, while there were negative direct effects of information (...) sharing on creative climate and on intrinsic motivation. This study suggests that information sharing and intrinsic motivation are important drivers for organizational creativity in a complex R&D; environment in the pharmaceutical industry. Implications of the model are discussed. (shrink)

Recent work in cognitive science and cognitive neuroscience has produced some surprising results that provide the materials for an empirical theory of concept acquisition which departs in significant ways from both radical nativism and classical empiricism. In this dissertation I develop a theory of how pre-linguistic infants acquire the capacity to construct causal concepts, in the period from 7 to 24 months of age. In humans the sophisticated modular capacity of the mammalian nervous system to track conditional frequencies of biologically (...) significant environmental variables feeds a higher system that detects causal properties. Human infants can perceptually discriminate some of these properties by 3 months of age. Joint attention and the other social cognitive capacities they develop in the second half of their first year provide the context in which babies develop their first true concepts. These require a degree of self-consciousness not required for perceptual categorization. Early concepts are causal and theoretical, providing powerful tools for induction. Their encoding, use, and updating may be facilitated by the special properties of causal Bayes net type architectures. It is as true of the baby's first concepts as of the complex concepts of a mature discipline that conceptual content is not read off from sensory experience. I attempt to show how a concept the content of which is underdetermined by sensory experience can nonetheless be empirically derived, in a process that endows it with normative semantic properties. (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)

The measurement of pain depends upon subjective reports, but we know very little about how research subjects or pain patients produce self-reported judgments. Representationalist assumptions dominate the field of pain research and lead to the critical conjecture that the person in pain examines the contents of consciousness before making a report about the sensory or affective magnitude of pain experience as well as about its nature. Most studies to date have investigated what Fechner termed “outer psychophysics”: the relationship between characteristics (...) of an external stimulus and the magnitude and nature of pain experience. In contrast, Fechner originally envisioned that “inner psychophysics” should investigate the relationship between physiological states and subjective experience. Despite the lack of established research tradition, inner psychophysics has a potential utility in elucidating underlying mechanisms for the production of phenomenal self-report. We illustrate this, using causal modeling analyses of the accuracy of self-reported pain ratings from our laboratory. We submit that the results are inconsistent with representationalist assumptions. Converging trends from several domains of consciousness studies seem to suggest that we need to abandon the unquestioned doctrine of representationalism and search for a more viable framework for understanding the generation of subjective self-report. (shrink)

In the following we consider the possibility of interpretating recent non-local interferometric experiments according to the De Broglie causal model. With the help of a simplified mathematical model based on wavelet analysis it is indeed possible to explain it in a causal way. Furthermore we show the distinctions between the two formalisms and discuss some experimental conditions that may make these differences evident.

The measurement of pain depends upon subjective reports, but we know very little about how research subjects or pain patients produce self-reported judgments. Representationalist assumptions dominate the field of pain research and lead to the critical conjecture that the person in pain examines the contents of consciousness before making a report about the sensory or affective magnitude of pain experience as well as about its nature. Most studies to date have investigated what Fechner termed “outer psychophysics”: the relationship between characteristics (...) of an external stimulus and the magnitude and nature of pain experience. In contrast, Fechner originally envisioned that “inner psychophysics” should investigate the relationship between physiological states and subjective experience. Despite the lack of established research tradition, inner psychophysics has a potential utility in elucidating underlying mechanisms for the production of phenomenal self-report. We illustrate this, using causal modeling analyses of the accuracy of self-reported pain ratings from our laboratory. We submit that the results are inconsistent with representationalist assumptions. Converging trends from several domains of consciousness studies seem to suggest that we need to abandon the unquestioned doctrine of representationalism and search for a more viable framework for understanding the generation of subjective self-report. (shrink)

While agreeing with Rose's reasoning about why the causes of organisms' behaviors cannot be reduced to the solely biological and molecular, this review questions Rose's uses of the terms “determinism” and “contingency”; his occasional seemingly cavalier acceptance as fact of unproven hypotheses about social and psychological phenomena; and his general disdain for the psychometric tradition and its causal modeling extensions.

Various social animal species have been noted to inhibit aggressive attacks when a conspecific displays submission cues. Blair (1993) has suggested that humans possess a functionally similar mechanism which mediates the suppression of aggression in the context of distress cues. He has suggested that this mechanism is a prerequisite for the development of the moral/conventional distinction; the consistently observed distinction in subject's judgments between moral and conventional transgressions. Psychopaths may lack this violence inhibitor. A causal model is developed showing how (...) the lack of this mechanism would explain the core behavioural symptoms associated with the psychopathic disorder. A prediction of such a causal model would be that psychopaths should fail to make the moral/conventional distinction. This prediction was confirmed. The implication of this finding for other theories of morality is discussed. (shrink)

During the past decades several philosophers of science and social scientists have been interested in the problems of causation. Recently attention has been given to probabilistic causation in dichotomous causal systems. The paper uses the basic features of probabilistic causation to argue that the causal modeling approaches developed by such researchers as Blalock (1964) and Duncan (1975) can provide, when an additional assumption is added, adequate qualitative measures of one variableś causal influence upon another. Finally, some of the difficulties and (...) issues involved in developing adequate quantitative measures are discussed, and it is concluded that the causal modeling measures cannot provide adequate quantitative measures. (shrink)

Over the past two decades, several consistent procedures have been designed to infer causal conclusions from observational data. We prove that if the true causal network might be an arbitrary, linear Gaussian network or a discrete Bayes network, then every unambiguous causal conclusion produced by a consistent method from non-experimental data is subject to reversal as the sample size increases any finite number of times. That result, called the causal flipping theorem, extends prior results to the effect that causal discovery (...) cannot be reliable on a given sample size. We argue that since repeated flipping of causal conclusions is unavoidable in principle for consistent methods, the best possible discovery methods are consistent methods that retract their earlier conclusions no more than necessary. A series of simulations of various methods across a wide range of sample sizes illustrates concretely both the theorem and the principle of comparing methods in terms of retractions. (shrink)

The network approach to psychopathology is becoming increasingly popular. The motivation for this approach is to provide a replacement for the problematic common cause perspective and the associated latent variable model, where symptoms are taken to be mere effects of a common cause (the disorder itself). The idea is that the latent variable model is plausible for medical diseases, but unrealistic for mental disorders, which should rather be conceptualized as networks of directly interacting symptoms. We argue that this rationale for (...) the network approach is misguided. Latent variable (or common cause) models are not inherently problematic, and there is not even a clear boundary where network models end and latent variable (or common cause) models begin. We also argue that focusing on this contrast has led to an unrealistic view of testing and finding support for the network approach, as well as an oversimplified picture of the relationship between medical diseases and mental disorders. As an alternative, we point out more essential contrasts, such as the contrast between dynamic and static modeling approaches, that can provide a better framework for conceptualizing mental disorders. Finally, we discuss several topics and open problems that need to be addressed in order to make the network approach more concrete and to move the field of psychological network research forward. (shrink)

John Campbell has claimed that the interventionist account of causation must be amended if it is to be applied to causation in psychology. The problem, he argues, is that it follows from the so-called ‘surgical’ constraint that intervening on psychological states requires the suspension of the agent’s rational autonomy. In this paper, I argue that the problem Campbell identifies is in fact an instance of a wider problem for interventionism, extending beyond psychology, which I call the problem of ‘abrupt transitions’. (...) I then defend a solution to the problem, which replaces the surgical constraint with a weaker constraint on interventions that nevertheless does all the work the surgical constraint was designed to do. I conclude by exploring some interesting consequences of this weaker constraint for causation in psychology. (shrink)

It is often natural to compare two events by describing one as ‘more of a cause’ of some effect than the other. But what do such comparisons amount to, exactly? This paper aims to provide a guided tour of the recent literature on ‘degrees of causation’. Section 2 looks at what I call ‘dependence measures’, which arise from thinking of causes as difference-makers. Section 3 looks at what I call ‘production measures’, which arise from thinking of causes as jointly sufficient (...) for their effects. Finally, section 4 examines the important question of whether there is any sense in which an agent is more responsible for an outcome in virtue of her action being more of a cause of it. I describe a puzzle that emerges from this question, and explore various strategies for resolving it. (shrink)

The purpose of this study was to examine how moral reasoning develops for 236 students enrolled in either a diversity course or a management course. These courses were compared based on the level of diversity inclusion and type of pedagogy employed in the classroom. We used causal modelling to compare the two types of courses, controlling for the effects of demographic (i.e., race, gender), curricular (i.e., previous course-related diversity learning) and pedagogical (i.e., active learning) covariates. Results showed that students enrolled (...) in the diversity course demonstrated higher levels of moral reasoning than students enrolled in the management course. In addition, results show that previous diversity courses as well as current enrolment in a diversity course contributed to moral reasoning gains. Implications are discussed. (shrink)

I argue against counterfactual theories of causation , develop a pragmatic version of the Covering Law view, and offer a causal theory of counterfactuals. ;The initial idea of CTCs is that event a causes event b if b would not have occurred, if a had not occurred. David Lewis proposes this view as a solution to problems of "effects" and "epiphenomena". I argue that CTCs cannot solve these problems. Covering Law theories can, but only by rejecting traditional Humean accounts of (...) laws. ;Following Nancy Cartwright , I argue that there is no a priori route from probabilistic association to causal relevance, and that distinguishing causal relevance from mere association requires further causal knowledge. Some recent literature on causal modelling is also examined. ;Much causal knowledge concerns rough, approximate ceteris paribus laws and generalizations. I examine these and argue that they involve important pragmatic components. ;Many aspects of intuitive causal judgment and reasoning are explained by ceteris paribus causes. For example, many causes are preventable, and only ceteris paribus causes could be prevented. Ceteris paribus causes are capacities, which make similar contributions to different circumstances, and interact with each other . The pragmatic features of ceteris paribus laws hold equally of ceteris paribus causes. ;Singular and generic causes are unified by appeal to the a posteriori impossibility of "action at a distance". The locality of causal relations requires that causes act by continuous intervening processes. Singular causes and effects are instances of generic causal process types. ;Numerous cases of unclear and conflicting intuitions about causes and effects are explained by contextual factors . ;I explain away the attraction of CTCs by offering a causal account of counterfactuals. It is natural to express causal facts using counterfactuals because the truth conditions of counterfactuals are direct consequences of causal facts. (shrink)

This paper presents the econometric approach to causal modeling. It is motivated by policy problems. New causal parameters are defined and identified to address specific policy problems. Economists embrace a scientific approach to causality and model the preferences and choices of agents to infer subjective (agent) evaluations as well as objective outcomes. Anticipated and realized subjective and objective outcomes are distinguished. Models for simultaneous causality are developed. The paper contrasts the Neyman-Rubin model of causality with the econometric approach.