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Results for “causality”

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PubMed2006

Factors associated with establishing a causal diagnosis for children with cardiomyopathy.

OBJECTIVE: The goal was to identify the clinical variables associated with establishing a cause of cardiomyopathy in children. METHODS: The Pediatric Cardiomyopathy Registry contains clinical and causal testing information for 916 children who were diagnosed as having cardiomyopathy in North America between 1990 and 1995. Children with a causal diagnosis were compared with those without with respect to several demographic, clinical, and causal testing variables. RESULTS: Cardiomyopathy was 1 of 4 types, hypertrophic (34.2%), dilated (53.8%), restrictive (3.2%), or other or mixed (8.9%). Only one third of cases had a known cause. Children with a known cause for hypertrophic cardiomyopathy were more likely to be female, to be relatively smaller, to present with congestive heart failure, and to have increased left ventricular posterior wall thickness without outflow tract obstruction. For dilated cardiomyopathy, a known cause was associated with older age, lower heart rate, smaller left ventricular dimensions, and greater shortening fraction. Family history of cardiomyopathy predicted a significantly higher rate of causal diagnoses for all cardiomyopathy types, whereas family histories of genetic syndromes and sudden death were also predictive of a cause for hypertrophic and dilated cardiomyopathies. For hypertrophic cardiomyopathy, only blood and urine testing was associated with a causal diagnosis, whereas both viral serologic testing or culture and endomyocardial biopsy were independent predictors of a causal diagnosis in dilated cardiomyopathy. CONCLUSIONS: Certain patient characteristics, family history, echocardiographic findings, laboratory testing, and biopsy were associated significantly with establishing a cause of pediatric cardiomyopathy. Early endomyocardial biopsy should be considered strongly for children with dilated cardiomyopathy, for definitive diagnosis of viral myocarditis. Although not widely used, skeletal muscle biopsy may yield a cause for some patients with hypertrophic cardiomyopathy and for patients suspected of having a mitochondrial disorder.

Biopsy↗

PubMed2005

Causation and causal inference in epidemiology.

Concepts of cause and causal inference are largely self-taught from early learning experiences. A model of causation that describes causes in terms of sufficient causes and their component causes illuminates important principles such as multi-causality, the dependence of the strength of component causes on the prevalence of complementary component causes, and interaction between component causes. Philosophers agree that causal propositions cannot be proved, and find flaws or practical limitations in all philosophies of causal inference. Hence, the role of logic, belief, and observation in evaluating causal propositions is not settled. Causal inference in epidemiology is better viewed as an exercise in measurement of an effect rather than as a criterion-guided process for deciding whether an effect is present or not.

Causality↗

PubMed1999

"Controversies in epidemiology", teaching causality in context at the University at Albany, School of Public Health.

Social inequalities relate not only to disparities in health but also are the social context for theories of disease causality being legitimized or denied. In the discipline of epidemiology, conventional discussions on whether or not a given exposure "causes" a specific disease are framed almost exclusively as debates of validity and whether there is sufficient accumulation of evidence to satisfy "Hill's Causal Criteria". However, the way in which the conceptualization of disease processes is restricted to conform to the current causal paradigm, which is based on socially determined ideas of individualism, reductionism, monocausality and the legitimacy of social inequalities, can be identified as a fundamental assumption underlying conventional epidemiological debates of causality. Any argument that social inequalities are an important determinant of poor public health must also explicitly critique the current causal paradigm, which disallows epidemiologists from conceptualizing social relationships as causal of poor health in populations.

Causality↗

PubMed2002

Mechanisms of predictive and diagnostic causal induction.

In predictive causal inference, people reason from causes to effects, whereas in diagnostic inference, they reason from effects to causes. Independently of the causal structure of the events, the temporal structure of the information provided to a reasoner may vary (e.g., multiple events followed by a single event vs. a single event followed by multiple events). The authors report 5 experiments in which causal structure and temporal information were varied independently. Inferences were influenced by temporal structure but not by causal structure. The results are relevant to the evaluation of 2 current accounts of causal induction, the Rescorla-Wagner (R. A. Rescorla & A. R. Wagner, 1972) and causal model theories (M. R. Waldmann & K. J. Holyoak, 1992).

Analysis of Variance↗

PubMed1996

Human causal discovery from observational data.

Utilizing Bayesian belief networks as a model of causality, we examined medical students' ability to discover causal relationships from observational data. Nine sets of patient cases were generated from relatively simple causal belief networks by stochastic simulation. Twenty participants examined the data sets and attempted to discover the underlying causal relationships. Performance was poor in general, except at discovering the absence of a causal relationship. This work supports the potential for combining human and computer methods for causal discovery.

Bayes Theorem↗

PubMed1981

Correspondences between biomathematical and causal models for clinical decision making.

Due to incompleteness and other uncertainties, biomathematical models are unsuitable for direct use in clinical decision making. In this research work, we develop a procedure to derive clinical decision-making causal models from mathematical representation. The process involves obtaining the determination ordering for an incompletely specified system of equations. The concept of determination ordering is extended to dynamic systems of equations, in order to derive clinically usable models. The procedure to transform biomathematical models into causal representation has been machine-implemented for fluid flow models of the eye. A case-structured natural language system (CHRONOS) has been developed to accept, process, and store causal as well as biomathematical models. The system obtains the determination ordering for the biomathematical models and stores their causal representation. The system has the capability to compare the causal models. The deductive capabilities of the system can be used by a clinician to consult the diagnostic reasonings of the biomathematical and causal models.

Aqueous Humor↗

PubMed1998

Causal modeling of epidemiological data on psychiatric disorders.

This paper reviews the logic of causal inference from epidemiological data. I maintain that the clearest causal statements can be made when the philosophical causal principles of association, direction and isolation are upheld in epidemiological research. After reviewing the argument by Holland that only experimental manipulation affords clear causal claims, I examine the utility of structural equation models and longitudinal methods for making causal claims from non-experimental data. This examination leads to the conclusion that mental health epidemiologists should begin to incorporate intervention trials into the last phases of their research programmes when they want to make strong causal claims.

Humans↗

PubMed1987

Sources of meaning in the acquisition of complex syntax: the sample case of causality.

The study reported here is concerned with how children acquire complex sentences for expressing their beliefs about causally related events, in the transition in language development from simple to complex syntax. Subjects were three girls and four boys, observed longitudinally from 26 to 38 months of age in their homes. Data analysis began with those observations in which each child began to produce causally related propositions without syntactic connectives, and continued until the children were about 3 years old. Two broad categories of causal meaning were expressed in the children's causal statements. Objective meaning concerned means-end and consequence relations that were evidential and fixed in the physical world. Subjective meaning expressed causal connections concerned with personal, affective, or sociocultural beliefs. While most of the children's statements expressed subjective meaning overall, the acquisition of syntactic connectives was associated with objective meaning. These results are discussed in terms of the development of these children's understanding of causality and the acquisition of increasingly complex language.

Age Factors↗

PubMed2005

Individual differences in causal learning and decision making.

In judgment and decision making tasks, people tend to neglect the overall frequency of base-rates when they estimate the probability of an event; this is known as the base-rate fallacy. In causal learning, despite people's accuracy at judging causal strength according to one or other normative model (i.e., Power PC, DeltaP), they tend to misperceive base-rate information (e.g., the cause density effect). The present study investigates the relationship between causal learning and decision making by asking whether people weight base-rate information in the same way when estimating causal strength and when making judgments or inferences about the likelihood of an event. The results suggest that people differ according to the weight they place on base-rate information, but the way individuals do this is consistent across causal and decision making tasks. We interpret the results as reflecting a tendency to differentially weight base-rate information which generalizes to a variety of tasks. Additionally, this study provides evidence that causal learning and decision making share some component processes.

Cognition↗

PubMed2006

The role of activity in visual impressions of causality.

Phenomenal causality is an illusion built on an incomplete perception. It is an illusion because we can have visual impressions of causality when no interaction between objects is actually taking place. It is an illusion built on an incomplete perception because causality as we understand it neglects some factors involved in objective descriptions of interactions between objects in terms of the laws of mechanics. So, why don't we perceive object interactions in accordance with the laws of mechanics? I first consider what kinds of things can and cannot be causes perceptually, arguing that active objects can be causes and non-moving objects cannot be. Then, I argue that causal understanding originates with what we have the most direct experience of, our own actions on objects, and extends out from this point of origin to other domains of causality by a form of schema matching the interpretation of stimulus input by matching to abstracted stored representations of experiences. Schema matching raises the possibility of many more kinds of phenomenal causality than have hitherto been considered, and I conclude by suggesting some possibilities.

Cognition↗

PubMed2006

The perception of causality in infancy.

Michotte proposed a rationalist theory of the origin of the human capacity to represent causal relations among events. He suggested that the input analyzer that underlies the causal perception in launching, entraining, and expulsion events is innate and is the ultimate source of all causal representations. We review the literature on infant causal representations, providing evidence that launching, entraining and expulsion events are interpreted causally by young infants. However, there is as of yet no good evidence that these representations are innate. Furthermore, there is considerable evidence that these representations are not the sole source of the human capacity for causal representation.

Cognition↗

PubMed2005

Brain mechanisms underlying perceptual causality.

Functional magnetic resonance imaging (fMRI) was used to examine the neural correlates of perceptual causality. Participants were imaged while viewing alternating blocks of causal events in which a ball collides with, and causes movement of another ball, versus non-causal events in which a spatial or a temporal gap precedes the movement of a second ball. There were significantly higher levels of relative activation in the right middle frontal gyrus and the right inferior parietal lobule for causal relative to non-causal events. Furthermore, when the differential effects of spatial and temporal incontiguities were subtracted from the contiguous stimuli, we observed both common (right prefrontal) and unique (right parietal and right temporal) regions of activation as a function of spatial and temporal processing of contiguity, respectively. Taken together, these data provide a means to help determine how the visual system extracts causality from dynamic visual information in the environment using spatial and temporal cues.

Adult↗

PubMed2005

Frequency decomposition of conditional Granger causality and application to multivariate neural field potential data.

It is often useful in multivariate time series analysis to determine statistical causal relations between different time series. Granger causality is a fundamental measure for this purpose. Yet the traditional pairwise approach to Granger causality analysis may not clearly distinguish between direct causal influences from one time series to another and indirect ones acting through a third time series. In order to differentiate direct from indirect Granger causality, a conditional Granger causality measure in the frequency domain is derived based on a partition matrix technique. Simulations and an application to neural field potential time series are demonstrated to validate the method.

Algorithms↗

PubMed2006

Recursive causality in evolution: a model for epigenetic mechanisms in cancer development.

Interactions between adaptative and selective processes are illustrated in the model of recursive causality as defined in Rupert Riedl's systems theory of evolution. One of the main features of this theory also termed as theory of evolving complexity is the centrality of the notion of 'recursive' or 'feedback' causality - 'the idea that every biological effect in living systems, in some way, feeds back to its own cause'. Our hypothesis is that "recursive" or "feedback" causality provides a model for explaining the consequences of interacting genetic and epigenetic mechanisms which are known to play a key role in development of cancer. Epigenetics includes any process that alters gene activity without changes of the DNA sequence. The most important epigenetic mechanisms are DNA-methylation and chromatin remodeling. Hypomethylation of so-called oncogenes and hypermethylation of tumor suppressor genes appear to be critical determinants of cancer. Folic acid, vitamin B12 and other nutrients influence the function of enzymes that participate in various methylation processes by affecting the supply of methyl groups into a variety of molecules which may be directly or indirectly associated with cancerogenesis. We present an example from our own studies by showing that vitamin D3 has the potential to de-methylate the osteocalcin-promoter in MG63 osteosarcoma cells. Consequently, a stimulation of osteocalcin synthesis can be observed. The above mentioned enzymes also play a role in development and differentiation of cells and organisms and thus illustrate the close association between evolutionary and developmental mechanisms. This enabled new ways to understand the interaction between the genome and environment and may improve biomedical concepts including environmental health aspects where epigenetic and genetic modifications are closely associated. Recent observations showed that methylated nucleotides in the gene promoter may serve as a target for solar UV-induced mutations of the p53 tumor suppressor gene. This illustrates the close interaction of genetic and epigenetic mechanisms in cancerogenesis resulting from changes in transcriptional regulation and its contribution to a phenotype at the micro- or macroevolutionary level. Above-mentioned interactions of genetic and epigenetic mechanisms in oncogenesis defy explanation by plain linear causality, things like the continuing adaptability of complex systems. They can be explained by the concept of recursive causality and has introduced molecular biology into the realm of cognition science and systems theory: based on the notion of so-called feedback- or recursive causality a model for epigenetic mechanisms with relevance for oncology and biomedicine is provided.

Animals↗

PubMed2000

Verbal and visual causal arguments.

The present paper analyzes how verbalizations and visualizations can be used to justify and dispute causal claims. The analysis is based on a taxonomy of 27 causal arguments as they appear in ordinary language. It is shown how arguments from spatio-temporal contiguity, covariation, counterfactual necessity, and causal mechanisms, to name only a few, are visualized in persuasive uses of tables, graphs, time series, causal diagrams, drawings, maps, animations, photos, movies, and simulations. The discussion centers on how these visual media limit the argumentative moves of justifying, disputing, and qualifying claims; how they constrain the representation of observational, explanatory, and abstract knowledge in the premises of causal arguments; and how they support and externalize argument-specific inferences, namely generalizations, comparisons, mental simulations, and causal explanations.

Humans↗

PubMed2002

Exploratory causal modeling in epidemiology: are all factors created equal?

The purpose of this study was to demonstrate the consequences of analyzing sequentially caused relationships with models assuming equally proximate causation. Monte Carlo simulations of data with well defined causations were performed. The logistic modeling approach was strongly misleading if a distant causal factor was treated as a factor being equally distant to the outcome as a proximal causal factor. In contrast, simple pathway analysis was able to correctly identify the true causation. In causal pathways, the relative risk of an intermediate cause with respect to the outcome needs to have a certain magnitude for the effect of the distant variable to be passed on. The results further show that the true relative risk of the distant variable is not dependent on its baseline prevalence. In contrast, the prevalence of the intermediate variable must be small enough to carry the influence of the distant variable through the causal chain. Practical epidemiologic exploration of etiological factors is presently dominated by stepwise multiple regression. This type of exploration is not model free but is often intuitively based on the structural assumption of equal proximity of all potential factors to the outcome. Equal proximity, however, is not likely in many etiologies, especially not if the causal factors under consideration are of different quality, like psychological and biological factors. In cases of causal pathways with some factors more distant and others more proximal to the outcome, the former tend to be dismissed by equal proximity modeling. Upstream exploration of more distant etiological factors is hindered by endemic stepwise multiple regression modeling, treating all variables as being equal in proximity to the outcome.

Confounding Factors, Epidemiologic↗

PubMed1996

Childhood parental loss and alcoholism in women: a causal analysis using a twin-family design.

Childhood parental loss may be an important risk factor for psychiatric illness in adulthood. While this association has been carefully examined for depression, little is known about the role of parental loss in predisposing to alcoholism. We examined an epidemiological sample of female twin pairs with the same history of continuity or disruption in parent-child relationships (N=1018 pairs; mean age 30 years), using a range of definitions of alcoholism. Childhood parental loss through separation, but not death, substantially increased the risk in adulthood for all definitions of alcoholism. Furthermore, both paternal and maternal alcoholism substantially increased the probability of parental separation from their children. Proposing a structural equation twin-family model that incorporates childhood parental loss as a specified environmental risk factor, we examined how much of the association between childhood parental loss and alcoholism was causal (i.e. mediated by environmental factors) v. non-causal (mediated by genetic factors, with parental loss serving as an index of parental genetic susceptibility to alcoholism). Both the causal and non-causal paths were significant for all definitions of alcoholism. However, the causal-environmental pathway consistently accounted for most of the association. While a significant proportion of the association is due to non-causal genetic mechanisms, childhood parental loss (or the familial discord that precedes or follows it) is probably a direct and significant environmental risk factor for the development of alcoholism in women.

Adult↗

PubMed1983

Specious causal attributions in the social sciences: the reformulated stepping-stone theory of heroin use as exemplar.

The claims based on causal models employing either statistical or experimental controls are examined and found to be excessive when applied to social or behavioral science data. An exemplary case, in which strong causal claims are made on the basis of a weak version of the regularity model of cause, is critiqued. O'Donnell and Clayton claim that in order to establish that marijuana use is a cause of heroin use (their "reformulated stepping-stone" hypothesis), it is necessary and sufficient to demonstrate that marijuana use precedes heroin use and that the statistically significant association between the two does not vanish when the effects of other variables deemed to be prior to both of them are removed. I argue that O'Donnell and Clayton's version of the regularity model is not sufficient to establish cause and that the planning of social interventions both presumes and requires a generative rather than a regularity causal model. Causal modeling using statistical controls is of value when it compels the investigator to make explicit and to justify a causal explanation but not when it is offered as a substitute for a generative analysis of causal connection.

Heroin Dependence↗