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I argue for the causal character of modeling in data-intensive science, contrary to widespread claims that big data is only concerned with the search for correlations. After discussing the concept of data-intensive science and introducing two examples as illustration, several algorithms are examined. It is shown how they are able to identify causal relevance on the basis of eliminative induction and a related difference-making account of causation. I then situate data-intensive modeling within a broader framework of an epistemology of scientific knowledge. In particular, it is shown to lack a pronounced hierarchical, nested structure. The significance of the transition to such 'horizontal' modeling is underlined by the concurrent emergence of novel inductive methodology in statistics such as non-parametric statistics. Data-intensive modeling is well equipped to deal with various aspects of causal complexity arising especially in the higher level and applied sciences., Author(s): Wolfgang Pietsch[sup.1] Author Affiliations: (1) Munich Center for Technology in Society, Technische Universität München, Arcisstr. 21, 80333, Munich, Germany Introduction For some time, computer scientists have been speaking of [...]

Although it is intuitively plausible that a job seeker benefits by using contacts in her job search, the literature is plagued by theoretical disagreements and inconclusive empirical evidence. Single-firm studies consistently find that job seekers applying through referrals achieve better labor-market outcomes than job seekers applying without referrals, but the evidence from job-seeker studies is mixed. To solve this puzzle, we clarify the distinction between having social capital and using contacts as a search method. We present theoretical reasons to suggest that the lack of an association between a job seeker's social capital and whether or not she uses social networks to search for a job should not be taken to imply that job seekers who use social networks to search for jobs do not benefit from using contacts. We exploit a strategic research setting, the school-to-work transition of 291 university graduates who engaged in 3,112 contemporaneous job searches, to show that although a job seeker's social capital may not affect whether or not she uses contacts to search for a job, using contacts as a job-search method does improve her job-search outcomes. We conclude by discussing the implications of our findings for the literature on job search and social networks. Key words: labor markets; social capital; job search; network causality; school-to-work transition History: Received March 27, 2012; accepted August 28, 2012, by Jesper Sorensen, organizations. Published online in Articles in Advance April 22, 2013., Introduction 'It's not what you know, but who you know.' This is not only a nugget of popular wisdom and practical experience, but a concise summary of much of the [...]

In this paper I demonstrate that the causal structure of flagpole-like systems can be determined by application of causal graph theory. Additional information about the ordering of events in time or about how parameters of the systems of interest can be manipulated is not needed. Keywords Causality * Causal inference * Explanatory asymmetry, 1 Introduction The length of a flagpole's shadow can be causally explained by reference to the solar altitude and the flagpole's height, while neither the solar altitude nor the flagpole's [...]

Based on previous research on cultural differences in analytic and holistic reasoning, it was hypothesized in these studies that when explaining events, North Americans would be more likely than East Asians to expect causes to correspond in magnitude with those events (i.e., big events stem from big causes and small events stem from small causes). In a series of studies, Canadian and Chinese participants judged the likelihood that high- or low-magnitude events were caused by high- or low-magnitude causes. Overall, Canadians expected events and their causes to correspond in magnitude to a greater degree than did Chinese. Also, Canadians primed to reason holistically expected less cause-effect magnitude correspondence than did those primed to reason analytically.

This paper considers the media research with children from a social epidemiological perspective. I am especially concerned with comparing the types of research designs, standard measures and modeling frameworks used in media studies to those used in (social) epidemiology and prevention research more generally. Overall, the state of research on the effect of various media on outcomes in children is robust and promises to be on par with the best epidemiological research. The principal measurement deficiency lies in the measurement of potential confounders and effect modifiers. With respect to research designs, the principal deficiencies include neglect of effect-modification by recognized social groupings, contextual influences and social interaction. Substantial practical benefits may come from the implementation and evaluation of intervention programs, especially group-randomized trials. As for analyses, the principal weakness appears to be in extending causal inference into observational field studies. Keywords: causality; experiment; inference

Sociological and genetic theories of illness causation are described and propositions are drawn from different theoretical frames. Intradisciplinary tension in proximal and distal influences are essential in determining pathways to health and illness.

It has been proposed that causal power (defined as the probability with which a candidate cause would produce an effect in the absence of any other background causes) can be intuitively computed from cause--effect covariation information. Estimation of power is assumed to require a special type of counterfactual probe question, worded to remove potential sources of ambiguity. The present study analyzes the adequacy of such questions to evoke normative causal power estimation. The authors report that judgments to counterfactual probes do not conform to causal power and that they strongly depend on both the probe question wording and the way that covariation information is presented. The data are parsimoniously accounted for by an alternative model of causal judgment, the Evidence Integration rule. Keywords: causality, contingency, human learning, judgment, counterfactual

Computational models of analogy have assumed that the strength of an inductive inference about the target is based directly on similarity of the analogs and in particular on shared higher order relations. In contrast, work in philosophy of science suggests that analogical inference is also guided by causal models of the source and target. In 3 experiments, the authors explored the possibility that people may use causal models to assess the strength of analogical inferences. Experiments 1-2 showed that reducing analogical overlap by eliminating a shared causal relation (a preventive cause present in the source) from the target increased inductive strength even though it decreased similarity of the analogs. These findings were extended in Experiment 3 to cross-domain analogical inferences based on correspondences between higher order causal relations. Analogical inference appears to be mediated by building and then running a causal model. The implications of the present findings for theories of both analogy and causal inference are discussed. Keywords: analogical inference, causal model, inductive strength, preventive cause, generative cause

I argue on the basis of recent findings in neuroscience that consciousness is not a brain process, and then explore some alternative, non-reductive options concerning the metaphysical relationship between consciousness and the brain, such as weak and strong accounts of the emergence of consciousness and the constitution view of consciousness. I propose an Aristotelian account of the strong emergence of consciousness. This account motivates a wider ontology than reductive physicalism and makes reference to formal causation as a way explaining the causal power of consciousness. What is meant by formal causation, in this context, is that consciousness has the causal power to organize or control neuronal activity. This notion of causation is elaborated and supported by recent findings in the neurosciences. An advantage of this empirically informed approach is that proponents of the irreducibility of consciousness no longer need to rely upon conceptually based arguments alone, but can build a case against reductive physicalism that has a significant empirical foundation.

Michotte (1946/1063, p. 935) concluded that color 'has no bearing whatever on the question of qualitative causality.' Surprisingly, this claim has received little empirical investigation in the 60 years since its publication. In 2 experiments, 2 balls struck a cylinder and changed color (either continuously or in a stepped progression) for 2 s. After the 2-s interval, the cylinder disgorged a purple substance. Participants chose which of the 2 balls was most likely to have caused the disgorging effect. An object that changed color was favored as the cause much more often than one that did not, and participants generally preferred an object that reached its terminal color immediately before the effect over objects that reached their terminal colors earlier. When participants performed a causal ratings task, color change produced moderate judgments of causation, with little response differentiation as a function of color dynamics.

Recent evidence shows that outcome maximality (e.g., De Houwer, Beckers, & Glautier, 2002) and additivity training (e.g., Lovibond, Been, Mitchell, Bouton, & Frohard, 2003) have an influence on cue competition in human causal learning. This evidence supports the idea that cue competition is based on controlled reasoning processes rather than on automatic associative processes. Until now, however, all the evidence for controlled reasoning processes comes from studies with rather simple designs that involved only few cues and events. We conducted two experiments with a complex design involving 24 different cues. The results showed that outcome maximality and additivity training had an influence on cue competition but that this influence was more pronounced for forward cue competition than for retrospective cue competition. DOI:10.1080/17470210601002561

In an interference-between-cues design, the expression of a learned Cue A [right arrow] Outcome 1 association has been shown to be impaired if another cue, B, is separately paired with the same outcome in a second learning phase. In the present study, we assessed whether this interference effect is mediated by participants' previous causal knowledge. This was achieved by having participants learn in a diagnostic situation in Experiment 1a, and then by manipulating the causal order of the learning task in Experiments 1b and 2. If participants use their previous causal knowledge during the learning process, interference should only be observed in the diagnostic situation because only there we have a common cause (Outcome 1) of two disjoint effects, namely cues A and B. Consistent with this prediction, interference between cues was only found in Experiment la and in the diagnostic conditions of Experiments 1b and 2. DOI:10.1080/17470210601000961

The associative view of human causal learning argues that causation is attributed to the extent that the putative cause activates, via an association, a mental representation of the effect. That is, causal learning is a human analogue of animal conditioning. We tested this associative theory using a task in which a fictitious character suffered from two allergic reactions, rash ([O.sub.1]) and headache ([O.sub.2]). In a conditioned inhibition design with each of these two outcomes (A-[O.sub.1]/AX- and B-[O.sub.2]/BY-), participants were trained that one herbal remedy (X) prevented [O.sub.1] and that the other (Y) prevented [O.sub.2]. These inhibitory properties were revealed in a causal judgement summation test. In a subsequent categorization task, X was most easily categorized with [O.sub.1], and Y with [O.sub.2]. Thus, the categorization data indicated an excitatory X-[O.sub.1] and Y-[O.sub.2] association, the reverse of the inhibitory relationship observed on the causal judgement measure. A second experiment showed that this pattern of excitation and inhibition is dependent on intermixed A-[O.sub.1] and AX- trials. These results are problematic for the standard application of associative activation theories to causal judgement. We argue instead that the inhibition revealed in the causal judgement task reflects inferential reasoning, which relies, in part, on the ability of the cue in question to excite a representation of the outcome, as revealed in the categorization test. DOI:10.1080/17470210601002512

Studies performed by different researchers have shown that judgements about cue--outcome relationships are systematically influenced by the type of question used to request those judgements. It is now recognized that judgements about the strength of the causal link between a cue and an outcome are mostly determined by the cue--outcome contingency, whereas predictions of the outcome are more influenced by the probability of the outcome given the cue. Although these results make clear that those different types of judgement are mediated by some knowledge of the normative differences between causal estimations and outcome predictions, they do not speak to the underlying processes of these effects. The experiment presented here reveals an interaction between the type of question and the order of trials that challenges standard models of causal and predictive learning that are framed exclusively in associative terms or exclusively in higher order reasoning terms. However, this evidence could be easily explained by assuming the combined intervention of both types of process. DOI:10.1080/17470210601002520

Estimates of the causal efficacy of an event need to take into account the possible presence and influence of other unobserved causes that might have contributed to the occurrence of the effect. Current theoretical approaches deal differently with this problem. Associative theories assume that at least one unobserved cause is always present. In contrast, causal Bayes net theories (including Power PC theory) hypothesize that unobserved causes may be present or absent. These theories generally assume independence of different causes of the same event, which greatly simplifies modelling learning and inference. In two experiments participants were requested to learn about the causal relation between a single cause and an effect by observing their co-occurrence (Experiment 1) or by actively intervening in the cause (Experiment 2). Participants' assumptions about the presence of an unobserved cause were assessed either after each learning trial or at the end of the learning phase. The results show an interesting dissociation. Whereas there was a tendency to assume interdependence of the causes in the online judgements during learning, the final judgements tended to be more in the direction of an independence assumption. Possible explanations and implications of these findings are discussed. DOI:10.1080/17470210601002470

A hallmark feature of elemental associative learning theories is that multiple cues compete for associative strength when presented with an outcome. Cue competition effects have been observed in humans, both in forward and in backward blocking procedures (e.g., Shanks, 1985) and are often interpreted as evidence for an associative account of human causal learning (e.g., Shanks & Dickinson, 1987). Waldmann and Holyoak (1992), however, demonstrated that cue competition only occurs in predictive, and not diagnostic, learning paradigms. While unexplainable from an associative perspective, this asymmetry readily follows from structural considerations of causal model theory. In this paper, we show that causal models determine the extent of cue competition not only in forward but also in backward blocking designs. Implications for associative and inferential accounts of causal learning are discussed. DOI:10.1080/17470210601000839

The dynamics model, which is based on L. Talmy's (1988) theory of force dynamics, characterizes causation as a pattern of forces and a position vector. In contrast to counterfactual and probabilistic models, the dynamics model naturally distinguishes between different cause-related concepts and explains the induction of causal relationships from single observations. Support for the model is provided in experiments in which participants categorized 3-D animations of realistically rendered objects with trajectories that were wholly determined by the force vectors entered into a physics simulator. Experiments 1-3 showed that causal judgments are based on several forces, not just one. Experiment 4 demonstrated that people compute the resultant of forces using a qualitative decision rule. Experiments 5 and 6 showed that a dynamics approach extends to the representation of social causation. Implications for the relationship between causation and time are discussed. Keywords: causation, causal models, causal learning, knowledge structures, lexical semantics

Byline: Tjeerd Laar (1) Keywords: dimensioned realization; downward causation; downward determination; dynamical systems theory; mental causation Abstract: Dynamical systems theory (DST) is gaining popularity in cognitive science and philosophy of mind. Recently several authors (e.g. J.A.S. Kelso, 1995 A. Juarrero, 1999 F. Varela and E. Thompson, 2001) offered a DST approach to mental causation as an alternative for models of mental causation in the line of Jaegwon Kim (e.g. 1998). They claim that some dynamical systems exhibit a form of global to local determination or downward causation in that the large-scale, global activity of the system governs or constrains local interactions. This form of downward causation is the key to the DST model of mental causation. In this paper I evaluate the DST approach to mental causation. I will argue that the main problem for current DST approaches to mental causation is that they lack a clear metaphysics. I propose one metaphysical framework (Gillett, 2002a/b/c) that might deal with this deficiency. Author Affiliation: (1) Faculty of Philosophy, Radboud University Nijmegen, Erasmusgebouw 16.21, Erasmusplein 1, Postbus 9103, 6500, HD, Nijmegen, The Netherlands Article History: Registration Date: 10/07/2006 Online Date: 11/01/2007

Three experiments examined human processing of stimuli as predictors and causes. In Experiments 1A and 1B, two serial events that both preceded a third were assessed as predictors and as causes of the third event. Instructions successfully provided scenarios in which one of the serial (target) stimuli was viewed as a strong predictor but as a weak cause of the third event. In Experiment 2, participants' preexperimental knowledge was drawn upon in such a way that two simultaneous antecedent events were processed as predictors or causes, which strongly influenced the occurrence of overshadowing between the antecedent events. Although a tendency toward overshadowing was found between predictors, reliable overshadowing was observed only between causes, and then only when the test question was causal. Together with other evidence in the human learning literature, the present results suggest that predictive and causal learning obey similar laws, but there is a greater susceptibility to cue competition in causal than predictive attribution.

The relative validity effect (Wagner, Logan, Haberlandt, & Price, 1968) demonstrated that a strong cue or cause reduces responding to, or judgments of, a weaker cue or cause. We report two experiments with human subjects using relative validity preparations in which we investigate one- and two-cue competition effects. Previously, we investigated the effect using instrumental and Pavlovian conditioning preparations with rats. In the first experiment, we used a procedure analogous to the animal preparations. In the second experiment, we used a different probabilistic procedure. The results with humans and rats are very similar. In each species we find similar interference with processing the moderate predictor with one or with two strong competitors. These results are not well predicted by most associative models.

Construing all efficient causes as beginning and ceasing with their effects invites the dilemma that a given effect or event either always occurs or never occurs. One escapes the dilemma by distinguishing basic and subsidiary efficient causes, according temporal priority of causes to their effects in the case of the former. In the case of human making and doing, where the two efficient causes belong to the same subject, the two are supplemented by a final cause which serves to link or to mediate them. This it does by drawing out or actuating the subsidiary cause which exists potentially in the basic cause. Arguing from analogy, can it be argued that, just when basic and subsidiary efficient causes belong to the same non-human subject in nature, they must likewise be supplemented by final causes if the potentiality of subsidiary causes in basic causes is to be drawn out and made actual? Surprising though it might seem to some, the answer to this question is yes. (1) To show this, I first place efficient causes in a dilemma. Next I show how one escapes the dilemma by distinguishing basic and subsidiary efficient causes, making the former temporally precede the latter. These two causes in some cases need to be linked, and this, I argue, requires a cause of another type, a final cause.

Byline: Thomas A. Boylan (1), Paschal F. O'Gorman (1) Keywords: causal holism; Duhem-Quine thesis; economic methodology; pragmatism Abstract: Contemporary developments in economicmethodology have produced a vibrant agenda ofcompeting positions. These include, amongothers, constructivism, critical realism andrhetoric, with each contributing to the Realistvs. Pragmatism debate in the philosophies of thesocial sciences. A major development in theneo-pragmatist contribution to economicmethodology has been Quine's pragmatic assaulton the dogmas of empiricism, which are nowclearly acknowledged within contemporaryeconomic methodology. This assault isencapsulated in the celebrated Duhem-Quinethesis, which according to a number ofcontemporary leading philosophers of economics,poses a particularly serious methodologicalproblem for economics. This problem, asreflected in Hausman's analysis, consists ofthe inability of economics to learn fromexperience, thereby subverting the capacity totest economic theories. In this paper wedispute this position. Our argument is basedon a combination of Quine's holism with VanFraassen's constructive empiricism, especiallythe latter's analysis of empirical adequacy andhis pragmatic approach to explanation. Theresulting reorientation of economic methodologyrestores the capacity of economics to learnfrom experience and reinstates the imperativeof developing alternatives to orthodoxtheorizing in economics. Author Affiliation: (1) Department of Economics, National University of Ireland, Galway, Ireland Article History: Registration Date: 05/10/2004

By giving the proper emphasis to both radical skepticism and naturalism as two independent standpoints in Hume, I wish to propose a more satisfactory account of some of the more puzzling Humean claims on causation. I place these claims alternatively in either the philosophical standpoint of the radical skeptic or in the standpoint of everyday and scientific beliefs. I characterize Hume's radical skeptical standpoint in relation to Hume's perceptual model of the traditional theory of ideas, and I argue that Hume's radical skeptical argument concerning our causal inferences is inextricably linked to his skeptical argument concerning our idea of a necessary connection between cause and effect. I discuss Hume's naturalistic account of the origin of our idea of necessity and offer a new reading of Hume's two 'definitions' of cause. I argue along the way against central aspects of two opposing styles of interpretation--Norman Kemp Smith's and Annette Baier's, on the one hand, and Robert Fogelin's, on the other--that in my view do not appreciate the mutual autonomy of radical skepticism and naturalism in Hume.

This paper proposes that beyond introducing a more complex theory and subjecting it to empirical verification and fasification, finding the more precise time involved in causal relationship theoretically and methodologically can help science grow and develop.

The degree of causality of money to output is examined. Autoregressive conditional heteroskedastic errors make a standard test less effective. An approach using maximum likelihood and accounting for errors shows a small but significant money effect on output.

This research was concerned with naive ecology: ordinary people's understanding of causal processes in nature. Three experiments were performed to investigate the dissipation effect, defined by White (1997) as a tendency to judge that effects of a perturbation at a particular locus in an ecosystem or food web dissipate as they spread out from that locus. The experiments found further evidence for the generality' of the effect, ruled out confidence in judgment as a possible explanation, and suggested that the dissipation effect may be a general feature of causal reasoning about complex physical systems. There is a danger that the dissipation effect may underlie unintentional maltreatment of the environment, and there is a need for more research on this., How do ordinary people understand the workings of natural processes? What sort of understanding do they have of the factors that regulate population levels in animal and plant species, the [...]

Integration of contingency information underlies many cognitive tasks including causal, covariational, and probability judgments. The authors' feature-analytic approach was used to account for the findings that people differentially weight specific types of conjunctive information in causal (Experiment 1) and noncausal (Experiment 2) contingency judgments. These findings were explained in terms of positive-test and sufficiency-test biases, which were found in both judgment domains. The same biases, however, were not observed in normative conditional-probability judgments (Experiment 3). The authors argue that this discrepancy is owing to the differential clarity of normative criteria in these domains.

Research was conducted to study the psychometric characteristics of the Children's Attributional Style Questionnaire-Revised (CASQ-R) and to determine whether this shortened scale can serve as a reliable and valid substitute for the original CASQ. Focus of the study was on internal consistency reliability, criterion-related validity and test-retest reliability. Mean differences were tested across race, age and gender. Results indicate that the CASQ-R had a good criterion-related validity, fair test-retest reliability and moderate internal consistency reliability.

Research was conducted to examine explanatory style which people use to explain causes of deaths and disasters. This cognitive personality variable and attitudes of catastrophizing, self-blame and fatalism are correlated to depressive symptoms. Participants in the Terman Life-Cycle Study began in 1921-1922 completed questionnaires in 1936 and 1940 which were studied for explanatory style. Findings indicate that explanatory style had no specific link to death by illnesses while demonstrating the link between deaths and globality.