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Results for “causality”

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PubMed1996

Temporal reasoning for diagnosis in a causal probabilistic knowledge base.

We have added temporal reasoning to the Heart Disease Program (HDP) to take advantage of the temporal constraints inherent in cardiovascular reasoning. Some processes take place over minutes while others take place over months or years and a strictly probabilistic formalism can generate hypotheses that are impossible given the temporal relationships involved. The HDP has temporal constraints on the causal relations specified in the knowledge base and temporal properties on the patient input provided by the user. These are used in two ways. First, they are used to constrain the generation of the pre-computed causal pathways through the model that speed the generation of hypotheses. Second, they are used to generate time intervals for the instantiated nodes in the hypotheses, which are matched and adjusted as nodes are added to each evolving hypothesis. This domain offers a number of challenges for temporal reasoning. Since the nature of diagnostic reasoning is inferring a causal explanation from the evidence, many of the temporal intervals have few constraints and the reasoning has to make maximum use of those that exist. Thus, the HDP uses a temporal interval representation that includes the earliest and latest beginning and ending specified by the constraints. Some of the disease states can be corrected but some of the manifestations may remain. For example, a valve disease such as aortic stenosis produces hypertrophy that remains long after the valve has been replaced. This requires multiple time intervals to account for the existing findings. This paper discusses the issues and solutions that have been developed for temporal reasoning integrated with a pseudo-Bayesian probabilistic network in this challenging domain for diagnosis.

Artificial Intelligence↗

PubMed1996

Learning temporal probabilistic causal models from longitudinal data.

Medical problems often require the analysis and interpretation of large collections of longitudinal data in terms of a structural model of the underlying physiological behavior. A suitable way to deal with this problem is to identify a temporal causal model that may effectively explain the patterns observed in the data. Here we will concentrate on probabilistic models, that provide a convenient framework to represent and manage underspecified information; in particular, we will consider the class of Causal Probabilistic Networks (CPN). We propose a method to perform structural learning of CPNs representing time-series through model selection. Starting from a set of plausible causal structures and a collection of possibly incomplete longitudinal data, we apply a learning algorithm to extract from the data the conditional probabilities describing each model. The models are then ranked according to their performance in reconstructing the original time-series, using several scoring functions, based on one-step ahead predictions. In this paper we describe the proposed methodology through an example taken from the diabetes monitoring domain. The selection process is applied to a set of input-output models that generalize the class of ARX models, where the inputs are the insulin and meal intakes and the outputs are the blood glucose levels. Although the physiological process underlying this particular application is characterized by strong non-linearities and low data reliability, we show that it is possible to obtain meaningful results, in terms of conditional probability learning and model ranking power.

Algorithms↗

PubMed2004

Open-minded scepticism: inferring the causal mechanisms of human ageing from genetic perturbations.

Given the myriad of age-related changes and the many proposed mechanistic theories of ageing, a major problem in gerontology is distinguishing causes from effects. This review aims to identify and evaluate those mechanisms which have gathered experimental support in favour of seeing them as a cause rather than an effect of ageing. Recent results related to energy metabolism and ageing, the free radical and the DNA damage theories of ageing are reviewed and their predictions evaluated through a systems biology rationale. Crucial in this approach are genetic manipulations in animal models that enable researchers to discriminate causes from effects of ageing and focus on the causal structure of human ageing. Based on a system-level interpretation, the GH/IGF-1 axis appears the most likely explanation for caloric restriction and a possible causal mechanism of human ageing. Although much work remains to fully understand the human ageing process, there is little evidence that free radicals are a causal factor in mammalian ageing, though they may be involved in signalling pathways related to ageing. On the other hand, studying how the DNA machinery affects ageing appears a promising avenue for disclosing the human ageing process.

Aged↗

PubMed2026

Genetic evidence for a causal relationship between melatonin metabolism and depression.

To investigate the causal relevance of melatonin metabolism, which provides the biological basis for circulating melatonin levels, to specific depression symptom subtypes, we performed a targeted systematic review of melatonin metabolism pathways in the human brain and liver. Using two-sample Mendelian randomization (MR), we assessed the causal effects of metabolism pathways and/or individual genes on major depressive disorder (MDD) and nine symptom subtypes derived from Patient Health Questionnaire-9 (PHQ-9). Instrumental variables (IVs) were expression quantitative trait loci (eQTL) for eight individual genes, one synthesis route, and three degradation routes. Results were assessed using Bayesian colocalization and phenome-wide association analyses. At the pathway-level, the genetically proxied synthesis-route signal was associated with PHQ-9 Assessment 5 (PHQ9A5, OR: 0.89, 95% CI: 0.85-0.93), but sensitivity analyses suggested this association was primarily driven by TPH1 and may reflect serotonin-related biology. In contrast, higher brain melatonin degradation raised the risk of both PHQ9A1 (OR: 1.03, 95% CI: 1.02-1.04) and PHQ9A7 (OR: 1.03, 95% CI: 1.02-1.03). Within degradation, up-regulation of the kynurenine sub-pathway increased the odds of PHQ9A3 (OR: 1.05, 95% CI: 1.02-1.07), PHQ9A4 (OR = 1.04, 95% CI: 1.02-1.06) and PHQ9A7 (OR: 1.05, 95% CI: 1.02-1.07). Gene-level analyses were largely concordant, except for SULT1A1, whose higher expression was genetically protective for PHQ9A3 but risk-increased for PHQ9A1 and PHQ9A4. Overall, these results demonstrate that melatonin metabolism exerts symptom-specific and pathway-specific causal effects on depression. A stratified view of melatonin's role may help optimize the application of exogenous melatonin supplementation.

Melatonin↗

PubMed2025

Mendelian randomization reveals causal relationships between cytokines and male reproductive diseases.

This study aims to explore the causal links between cytokines and four male reproductive disorders, namely abnormal spermatozoa (AS), male infertility, erectile dysfunction (ED), and hyperplasia of prostate (HP), employing a two-sample Mendelian randomization (MR) approach. Genetic associations with male reproductive diseases were derived from the IEU OpenGWAS project, with cytokine data from two GWASs focused on the human proteome and cytokines. Estimations were derived using inverse variance weighting, MR-Egger regression, weighted median, weighted model, and simple mode. Furthermore, the robustness of the findings was evaluated through Cochran's Q-test, MR-Egger regression, and leave-one-out sensitivity analysis. Fifteen unique cytokines were identified as having causal relationships with the risk of four male reproductive disorders. Specifically, for AS, interleukin-22 (IL-22), IL-12, and macrophage migration inhibitory factor were negatively correlated with AS, while tumor necrosis factor β levels were positively correlated with AS. In the context of male infertility, IL-2 receptor antagonist levels, IL-34, and granulocyte-colony stimulating factor levels were positively linked to male infertility, whereas IL-21 showed a negative relationship. Regarding ED, IL-19, IL-1β, and eotaxin levels were negatively associated with ED risk, while macrophage inflammatory protein 1β (MIP-1β) levels and interferon gamma-induced protein 10 levels were positively associated. As for HP, stromal-cell-derived factor 1α levels and MIP-1α levels revealed negative associations with HP. In conclusion, this MR analysis revealed that several cytokines were causally associated with male reproductive diseases and could be valuable in offering new insights for further mechanistic and clinical investigations of cytokines-associated male reproductive diseases.

Male↗

PubMed2005

Mapping directed influence over the brain using Granger causality and fMRI.

We propose Granger causality mapping (GCM) as an approach to explore directed influences between neuronal populations (effective connectivity) in fMRI data. The method does not rely on a priori specification of a model that contains pre-selected regions and connections between them. This distinguishes it from other fMRI effective connectivity approaches that aim at testing or contrasting specific hypotheses about neuronal interactions. Instead, GCM relies on the concept of Granger causality to define the existence and direction of influence from information in the data. Temporal precedence information is exploited to compute Granger causality maps that identify voxels that are sources or targets of directed influence for any selected region-of-interest. We investigated the method by simulations and by application to fMRI data of a complex visuomotor task. The presented exploratory approach of mapping influences between a region of interest and the rest of the brain can form a useful complement to existing models of effective connectivity.

Attention↗

PubMed2006

Large-scale neural models and dynamic causal modelling.

Dynamic causal modelling (DCM) is a method for estimating and making inferences about the coupling among small numbers of brain areas, and the influence of experimental manipulations on that coupling [Friston, K.J., Harrison, L., Penny, W., 2003 Dynamic causal modelling. Neuroimage 19, 1273-1302]. Large-scale neural modelling aims to construct neurobiologically grounded computational models with emergent behaviours that inform our understanding of neuronal systems. One such model has been used to simulate region-specific BOLD time-series [Horwitz, B., Friston, K.J., Taylor, J.G., 2000. Neural modeling and functional brain imaging: an overview. Neural Netw. 13, 829-846]. DCM was used to make inferences about effective connectivity using data generated by a model implementing a visual delayed match-to-sample task [Tagamets, M.A., Horwitz, B., 1998. Integrating electrophysiological and anatomical experimental data to create a large-scale model that simulates a delayed match-to-sample human brain imaging study. Cereb. Cortex 8, 310-320]. The aim was to explore the validity of inferences made using DCM about the connectivity structure and task-dependent modulatory effects, in a system with a known connectivity structure. We also examined the effects of misspecifying regions of interest. Models with hierarchical connectivity and reciprocal connections were examined using DCM and Bayesian Model Comparison [Penny, W.D., Stephan, K.E., Mechelli, A., Friston, K.J., 2004. Comparing dynamic causal models. Neuroimage 22, 1157-1172]. This approach revealed strong evidence for those models with correctly specified anatomical connectivity. Furthermore, Bayesian model comparison favoured those models when bilinear effects corresponded to their implementation in the neural model. These findings generalised to an extended model with two additional areas and reentrant circuits. The conditional uncertainty of coupling parameter estimates increased in proportion to the number of incorrectly specified regions. These results highlight the role of neural models in establishing the validity of estimation and inference schemes. Specifically, Bayesian model comparison confirms the validity of DCM in relation to a well-characterised and comprehensive neuronal model.

Bayes Theorem↗

PubMed2026

Genetic evidence for causality of late chronotype on metabolic syndrome in East Asians and Europeans.

CONTEXT: The impact of chronotype-defined as an individuals' inherent preference of sleep timing-and its genetic determinants on metabolic syndrome (MetS) has been less studied. OBJECTIVE: This study investigated the causal relationship between late chronotype and MetS using Mendelian randomization (MR) analysis, based on data from the Taiwan Biobank (TWB) and parallel analyses in the UK Biobank (UKB). METHODS: A total of 36,845 participants from TWB served as the discovery cohort, and 235,639 participants from UKB served as the replication cohort. Late chronotype was defined in TWB as a preference for bedtime after midnight, and in UKB as self-report as being an 'evening' person. The association between late chronotype and MetS, along with its components, was evaluated in TWB, and validated in UKB. Genome-wide association analyses for late chronotype were first conducted in TWB and then meta-analyzed with UKB. Polygenic risk scores (PRS) for late chronotype were constructed and tested for association with MetS. Causality between late chronotype and MetS was examined using one-sample MR analysis in TWB and validated in UKB. RESULTS: Late chronotype was significantly associated with MetS, as well as with central obesity, hyperglycemia, and hypertriglyceridemia, in both TWB and UKB (all P < 0.0083, considering Bonferroni correction). The constructed PRS of late chronotype also showed significant associations with MetS and several of its components (several P < 0.0083, considering Bonferroni correction). Findings from the one-sample MR analysis indicated a potential causal effect of late chronotype on MetS. CONCLUSIONS: This study provides evidence of a robust association between late chronotype and MetS across populations of diverse ancestry, including Taiwanese and European.

Humans↗

PubMed2004

[Informational causality of the effect of drug signal].

Application of the inflammation theory in pharmacology leads to discuss the causality of the drug signal effect and compare the properties of informational causality with substantial and energetic causalities. This provides particular insight concerning drug efficacy.

Humans↗

PubMed2000

Causal cognition in a non-human primate: field playback experiments with Diana monkeys.

Crested guinea fowls (Guttera pucherani) living in West African rainforests give alarm calls to leopards (Panthera pardus) and sometimes humans (Homo sapiens), two main predators of sympatric Diana monkeys (Cercopithecus diana). When hearing these guinea fowl alarm calls, Diana monkeys respond as if a leopard were present, suggesting that by default the monkeys associate guinea fowl alarm calls with the presence of a leopard. To assess the monkeys' level of causal understanding, I primed monkeys to the presence of either a leopard or a human, before exposing them to playbacks of guinea fowl alarm calls. There were significant differences in the way leopard-primed groups and human-primed groups responded to guinea fowl alarm calls, suggesting that the monkeys' response was not directly driven by the alarm calls themselves but by the calls' underlying cause, i.e. the predator most likely to have caused the calls. Results are discussed with respect to three possible cognitive mechanisms - associative learning, specialized learning programs, and causal reasoning - that could have led to causal knowledge in Diana monkeys.

Animals↗

PubMed2003

Direct causation in the linguistic coding and individuation of causal events.

This research proposes a new theory of direct causation and examines how this concept plays a key role in the linguistic coding and individuation of causal events. According to the no-intervening-cause hypothesis, a causal chain can be described by a single-clause sentence and construed as a single event if there are no intervening causers between the initial causer and the final causee. Consistent with this hypothesis, participants used single-clause sentences (lexical causatives) more often than two-clause sentences (e.g. periphrastic causatives) for causal chains in which (1) the causer and causee touched (Experiments 1 and 2), and (2) an intervening entity could be construed as an enabling condition rather than another cause (Experiments 2-4). In addition, event judgments paralleled linguistic descriptions: chains that could be described with single-clause expressions were more often construed as single events than chains that could not (Experiments 1-3). Implications for languages other than English, for the linguistic coding of accidental outcomes and for the relationship between cognition and language in general are discussed.

Adolescent↗

PubMed1996

Children's use of counterfactual thinking in causal reasoning.

Research on children's causal thinking has emphasized the perception of temporal and spatial contiguity between cause and effect. However, our causal judgements often involve a contrast between a perceived sequence (A, then B) and a counterfactual case (in the absence of A, then not B). In three experiments, children's capacity for such counterfactual thinking was assessed. In Experiment I, children aged 3-5 years observed a sequence such as A causing B. Subsequently, they replied quite accurately to a question about a counterfactual sequence, for example: "What if A had not occurred, then B or not B?". In Experiment 2, children were asked about two counterfactual antecedents, one that would not have caused B, and one that (like the actual antecedent) would also have caused B. Children differentiated between the two types of antecedent. Finally, in Experiment 3, children heard stories in which the protagonist chose a course of action that led to a minor mishap (e.g., drawing with a black pen and getting inky fingers), having rejected an option that would have prevented it in experimental stories (e.g., using a pencil) or an option that would have led to an equivalent outcome in control stories (e.g., using a blue pen). Children aged 3 and 4 years often cited the failure to adopt another course of action as the cause of the mishap and, particularly in experimental stories, they focused on the rejection of the alternative option. Children's use of counterfactual thinking is discussed in relation to contemporary accounts of causal reasoning.

Child Development↗

PubMed2003

Statistical assessment of nonlinear causality: application to epileptic EEG signals.

In this study an information-theoretic test for general Granger causality is used to identify couplings and information transport between different brain areas during epileptic activities. This method can distinguish information that is actually exchanged between two systems from that due to the response to a common signal or past history. This is achieved by an appropriate conditioning of probabilities. Statistical assessment of causality is made from a nonparametric bootstrap test, whereas nonlinearity is assessed by a comparison with a linearized version of the causality index. The framework proposed here provides a useful and model free test to characterize interactions in intracranial electroencephalography (EEG) signals.

Electroencephalography↗

PubMed1998

Porque me tocó a mi? Mexican American diabetes patients' causal stories and their relationship to treatment behaviors.

This paper reports findings from an ethnographic study of self-care behaviors and illness concepts among Mexican-American non-insulin dependent diabetes mellitus (NIDDM) patients. Open-ended interviews were conducted with 49 NIDDM patients from two public hospital outpatient clinics in South Texas. They are self-identified Mexican-Americans who have had NIDDM for at least 1 yr, and have no major impairment due to NIDDM. Interviews focused on their concepts and experiences in managing their illness and their self-care behaviors. Clinical assessment of their glucose control was also extracted from their medical records. The texts of patient interviews were content analyzed through building and refining thematic matrixes focusing on their causal explanations and treatment behaviors. We found patients' causal explanations of their illness often are driven by an effort to connect the illness in a direct and specific way to their personal history and their past experience with treatments. While most cite biomedically accepted causes such as heredity and diet, they elaborate these concepts into personally relevant constructs by citing Provoking Factors, such as behaviors or events. Their causal models are thus both specific to their personal history and consistent with their experiences with treatment success or failure. Based on these findings, we raise a critique of the Locus of Control Model of treatment behavior prevalent in the diabetes education literature. Our analysis suggests that a sense that one's own behavior is important to the disease onset may reflect patients' evaluation of their experience with treatment outcomes, rather than determining their level of activity in treatment.

Adaptation, Psychological↗

PubMed2003

Return to work outcomes after work-related hand trauma: the role of causal attributions.

PURPOSE: This study examined the relationship between workers' judgments of responsibility for their accidents (causal attributions) and work-site avoidance after work-related injuries. METHODS: Ninety-two hand-injured workers referred for psychologic treatment of posttraumatic stress and depressive symptoms were assessed for their beliefs about the cause(s) of their accidents. Causal attributions were obtained before and after psychologic intervention. RESULTS: Workers who blamed coworkers or equipment for their injuries were more likely to resist returning to former work activities than workers who judged themselves responsible for their accidents. In addition those with relatively minor injuries were as much at risk for work-site avoidance as those with more severe injuries. Age, gender, and length of employment with current employer were unrelated to avoidance. CONCLUSIONS: These results suggest the importance of causal attributions as potential predictors of work-site avoidance after traumatic work-related hand injuries, and support the risk for psychologic symptom development after less-severe injuries.

Absenteeism↗

PubMed1998

The fuzzy cube and causal efficacy: representation of concomitant mechanisms in stroke.

Twentieth century medical science has embraced nineteenth century Boolean probability theory based upon two-valued Aristotelian logic. With the later addition of bit-based, von Neumann structured computational architectures, an epistemology based on randomness has led to a bivalent epidemiological methodology that dominates medical decision making. In contrast, fuzzy logic, based on twentieth century multi-valued logic, and computational structures that are content addressed and adaptively modified, has advanced a new scientific paradigm for the twenty-first century. Diseases such as stroke involve multiple concomitant causal factors that are difficult to represent using conventional statistical methods. We tested which paradigm best represented this complex multi-causal clinical phenomenon-stroke. We show that the fuzzy logic paradigm better represented clinical complexity in cerebrovascular disease than current probability theory based methodology. We believe this finding is generalizable to all of clinical science since multiple concomitant causal factors are involved in nearly all known pathological processes.

Journal Article↗

PubMed1999

Allergy prevalence and causal factors in the domestic environment: results of a random population survey in the United Kingdom.

BACKGROUND: Surveys of the prevalence of allergic diseases associated with domestic allergens (eg, asthma, rhinitis, hay fever, eczema, and dermatitis) are usually undertaken in restricted geographic areas. They are often based on specific demographic subgroups (eg, children) or are derived from treatment data (eg, GP consultations). Causal factors are seldom quantified in these reports. OBJECTIVE: This study was designed to quantify the prevalence of allergic disease among a representative cross-section of population in the United Kingdom. It was also designed to quantify the reported causal factors in terms of domestic allergen and irritant sources and activities leading to exposure. METHODS: Two thousand respondents were selected from across the UK. These respondents were interviewed using a short series of questions that formed part of a larger, more general omnibus questionnaire. The questions required respondents to report on diagnosed allergic disease and causal factors within their household, giving a total sample base of 5,609 people. RESULTS: Asthma and hay fever account for the majority of reported cases of allergic disease in the UK (12% and 10% of total household, respectively). Pollen, house dust mite excreta (HDM allergen), and pet dander are the three most common allergic triggers (42%, 27%, and 17% respectively). Dusting and being near pets each account for about 20% of the reported domestic activities triggering an allergic response. CONCLUSION: The feedback from the general population indicates asthma and hay fever to be the predominant allergic conditions, with dust and pet dander the most common sources of allergen/irritant. Key tasks such as changing bed linen and vacuuming were not perceived as being as important as dusting in terms of activities leading to reaction.

Activities of Daily Living↗

PubMed2000

Perceptual causality and animacy.

Certain simple visual displays consisting of moving 2-D geometric shapes can give rise to percepts with high-level properties such as causality and animacy. This article reviews recent research on such phenomena, which began with the classic work of Michotte and of Heider and Simmel. The importance of such phenomena stems in part from the fact that these interpretations seem to be largely perceptual in nature - to be fairly fast, automatic, irresistible and highly stimulus driven - despite the fact that they involve impressions typically associated with higher-level cognitive processing. This research suggests that just as the visual system works to recover the physical structure of the world by inferring properties such as 3-D shape, so too does it work to recover the causal and social structure of the world by inferring properties such as causality and animacy.

Journal Article↗