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PubMed2001

Primacy in causal strength judgments: the effect of initial evidence for generative versus inhibitory relationships.

The order in which people receive information has a substantial effect on subsequent judgment and inference. Our focus is on the order of covariation evidence in causal learning. The first experiment shows that the initial presentation of evidence suggesting a generative causal relationship (the joint presence or joint absence of cause and effect) leads to higher judged causal strength than does the initial presentation of evidence suggesting an inhibitory relationship (the presence of cause or effect in the absence of the other). Additional studies show that this primacy effect is unlikely to be due to fatigue or to an insufficient number of learning trials. These results are not readily explained by current contingency-based or associative theories of causal induction.

Adult↗

PubMed1992

Effect of local context of responding on human judgment of causality.

Two experiments examined the effect of various relationships between a response (pressing the space bar of a computer) and an outcome (a triangle flashing on a screen) on judgments of the causal effectiveness of the response. In Experiment 1, when responses were required to be temporarily isolated from each other prior to an outcome, ratings of the causal effectiveness of the responses were higher than in a condition in which the probability of an outcome following a response was the same but in which no temporal isolation was required. In Experiment 2, when a number of responses were required to be emitted temporally close to the outcome, ratings of the causal effectiveness of the responses were lower than in a condition in which the probability of an outcome following a response was the same but in which no temporal proximity was required. These results suggest that, in addition to the overall probability that an outcome will follow a response, the local context of responding at the time an outcome is presented is critical in influencing ratings of causal effectiveness.

Adult↗

PubMed1990

Causal modeling as a relevant approach to gerontological nursing.

Causal modeling involves the development of causal hypotheses to explain a particular phenomenon and the testing of causal assertions through systematic data collection and analysis procedures. Although more research is being conducted gerontological nurse scientists have generally failed to logically develop and systematically test theories that unify, organize, and consolidate knowledge into schemes that explain, describe, and predict. Causal modeling combines theory and research, and because the interpretation of data is possible only within the context of the proposed theory, it offers an important method for advancing the science while maintaining the specificity of the practice.

Causality↗

PubMed2005

Disentangling the causal relationships between work-home interference and employee health.

OBJECTIVES: The present study was designed to investigate the causal relationships between (time- and strain-based) work-home interference and employee health. The effort-recovery theory provided the theoretical basis for this study. METHODS: Two-phase longitudinal data (with a 1-year time lag) were gathered from 730 Dutch police officers to test the following hypotheses with structural equation modeling: (i) work-home interference predicts health deterioration, (ii) health complaints precede increased levels of such interference, and (iii) both processes operate. The relationship between stable and changed levels of work-home interference across time and their relationships with the course of health were tested with a group-by-time analysis of variance. Four subgroups were created that differed in starting point and the development of work-home interference across time. RESULTS: The normal causal model, in which strain-based (but not time-based) work-home interference was longitudinally related to increased health complaints 1 year later, fit the data well and significantly better than the reversed causal model. Although the reciprocal model also provided a good fit, it was less parsimonious than the normal causal model. In addition, both an increment in (strain-based) work-home interference across time and a long-lasting experience of high (strain-based) work-home interference were associated with a deterioration in health. CONCLUSIONS: It was concluded that (strain-based) work-home interference acts as a precursor of health impairment and that different patterns of (strain-based) work-home interference across time are related to different health courses. Particularly long-term experience of (strain-based) work-home interference seems responsible for an accumulation of health complaints.

Adult↗

PubMed2000

[Determinants of health and health policies. Part I. Causality in medicine and its modeling].

Causality is the relation between the antecedent and consequence. Association between those two notions represents causal determinacy allowing understanding the subject, and the theory of probability, which deals with supposed contradiction of the chance and necessity. Understanding how health can be impaired enables to precede the disease, to change its natural history and to cure it. Causality in medicine is based on the conception of complex action of biological, psychological and social factors, which may have either positive or negative effects on our health. Empirical data forming basis for the search of aetiology represent a conglomerate of causal and indifferent elements. To identify them, various models are employed (black box, Rubik's cube, Chinese box, model of multiple accidental phenomena and others). Appropriate epidemiological methods enable not only to determine the preference, but also to signalize fallacies of looking for the origin of disease. Permanent problem of the medicine reveals the existence of the objective accident and the uncertainty in the professional decision.

Causality↗

PubMed2005

Causal relationships in medicine.

The determination of causality is crucial in medicine: for example, a doctor who prescribes a therapeutic regimen believes that it will cure the disease. Causality in medicine is complex, since it can be difficult to establish a cause-effect relationship: arterial hypertension is a known risk factor for stroke, but most people with hypertension will not have a stroke, and most people who have strokes are normotensive. In this article we will define causality, then show how to determine a cause-effect relationship, and finally we will present the type of studies that can provide the strongest evidence on causality.

Causality↗

PubMed1994

A consumer's guide to causal modeling: Part II.

Causal modeling is a widely used technique for specifying a system of relationships among theoretical constructs. However, there are cautions or limitations that must be considered with this technique. Consumers of research that uses causal modeling techniques must evaluate the model testing results on several levels before using the findings in future research or in practice. A good fit between the model and the data does not necessarily mean that all the relationships are as posited. For the researcher intending to use causal modeling in an appropriate study, the most serious limitation of the technique is the large number of subjects required for the analysis, resulting in expensive and logistically complex studies. However, given the nature of many of the phenomena of interest to nursing, causal modeling often proves to be a highly useful technique for knowledge development.

Bias↗

PubMed1993

The explanatory role of events in causal and temporal reasoning in medicine.

The logic of time and the way we reason about time is intrinsically connected with the way we reason about causality. In this paper, we focus our attention on some of the less obvious ways in which reasoning about time and causality interact. It is explained why in temporal reasoning a firm distinction has to be made between the ontology, i.e., what happens, and the way we describe the ontology. Temporal events need to be redescribed in such a way that they causally explain why some of the events are followed by the others. While building a temporal/causal theory, certain events may be omitted, not because they do not play a causal role, but because they do not play an explanatory role. In doing so, it is possible to eliminate the distinction between theories representing time as dense, and theories that represent time as discrete.

Causality↗

PubMedDate not supplied

Causal attribution, perceived control, and adjustment in patients with lung cancer.

The relationships among causal attribution, perceived control, and adjustment to lung cancer were examined in 61 outpatients who had received a diagnosis of primary lung cancer. Data were collected using a structured interview and a self-report questionnaire. Both internal and external causal attributions were significantly positively correlated with perceived control. The relationship between internal causal attribution and perceived control was stronger. No significant relationships were found between perceived control and adjustment, although both internal and external causal attributions were significantly negatively correlated with aspects of adjustment. Recommendations are made for future research.

Adaptation, Psychological↗

PubMed2025

A Causal Effect of Serum 25(OH)D Level on Appendicular Muscle Mass: Evidence From NHANES Data and Mendelian Randomization Analyses.

BACKGROUND: Low serum vitamin D status was reported to be associated with reduced muscle mass; however, it is inconclusive whether this relationship is causal. This study used data from the National Health and Nutrition Examination Survey (NHANES) and two-sample Mendelian randomization (MR) analyses to ascertain the causal relationship between serum 25-hydroxyvitamin D [25(OH)D] and appendicular muscle mass (AMM). METHODS: In the NHANES 2011-2018 dataset, 11 242 participants (5588 males and 5654 females) aged 18-59 years old were included, and multivariant linear regression was performed to assess the relationship between 25(OH)D and AMM measured by dual-energy X-ray absorptiometry. In two-sample MR analysis, 167 single nucleotide polymorphisms significantly associated with serum 25(OH)D at the genome-wide association level (p < 5 × 10-8) were applied as instrumental variables (IVs) to assess vitamin D effects on AMM in the UK Biobank (417 580 Europeans) using univariable and multivariable MR (MVMR) models. RESULTS: In the NHANES dataset, serum 25(OH)D concentrations were positively associated with AMM (β = 0.013, SE = 0.001, p < 0.001) in all participants, after adjustment for age, race, season of blood collection, education, income, body mass index and physical activity. In stratification analysis by sex, males (β = 0.024, SE = 0.002, p < 0.001) showed more pronounced positive associations than females (β = 0.003, SE = 0.002, p = 0.024). In univariable MR, genetically higher serum 25(OH)D levels were positively associated with AMM in all participants (β = 0.049, SE = 0.024, p = 0.039) and males (β = 0.057, SE = 0.025, p = 0.021), but only marginally significant in females (β = 0.043, SE = 0.025, p = 0.090) based on IVW models was noticed. No significant pleiotropy effects were detected for the IVs in the two-sample MR investigations. In MVMR analysis, a positive causal effect of 25(OH)D on AMM was observed in the total population (β = 0.116, SE = 0.051, p = 0.022), males (β = 0.111, SE = 0.053, p = 0.036) and females (β = 0.124, SE = 0.054, p = 0.021). CONCLUSIONS: Our results suggested a positive causal effect of serum 25(OH)D concentration on AMM; however, more researches are warranted to unveil the underlying biological mechanisms and evaluate the effects of vitamin D intervention on AMM.

Humans↗

PubMed2005

Causality assessment of adverse drug reactions: comparison of the results obtained from published decisional algorithms and from the evaluations of an expert panel.

PURPOSE: To compare the results of causality assessments of reported adverse drug reactions (ADR's) obtained from decisional algorithms with those obtained from an expert panel using the WHO global introspection method (GI) and to further evaluate the influence of confounding variables on algorithms ability in assessing causality. METHOD: Two hundred sequentially reported ADR's were included in this study. An independent researcher used algorithms, while an expert panel assessed the same reports using the GI, both aimed at evaluating causality. Reports were divided into three groups according to the presence, absence or lack of information on confounding variables. RESULTS: For the total sample, observed agreements between decisional algorithms compared with GI varied from 21% to 56%, average of 47%. When confounding variables were taken into account, agreements varied between 41% and 69%, average of 58%; 8% and 65%, average of 46% and 15% and 53%, average of 42% accordingly to the absence, lack of information or presence of confounding variables, respectively. The extend of reproducibility beyond chance was low for the total sample (average Kappa = 0.26) and within the groups considered. CONCLUSION: The overall observed agreement between algorithm and GI was moderate although poorly different from chance, confounding variables being a shortcoming of algorithms ability in assessing causality.

Adverse Drug Reaction Reporting Systems↗

PubMedDate not supplied

Estimating the causal effects of smoking.

An important application of statistics in recent years has been to address the causal effects of smoking. There is little doubt that there are health risks associated with smoking. However, more general issues concern the causal effects due to the alleged misconduct of the tobacco industry or due to programmes designed to curtail tobacco use. To address any such causal question, assumptions must be made. Although some of the issues are well known in the statistical and epidemiological literature, there does not appear to be a unified treatment that provides prescriptive guidance on the estimation of these causal effects with explication of the needed assumptions. A 'conduct attributable fraction' is derived, which allows for arbitrary changes in smoking and non-smoking health care expenditure related factors in a counterfactual world without the alleged misconduct, and therefore generalizes the traditional 'smoking attributable fraction'. The formulation presented here, although described for the problem of estimating excess health care expenditures due to the alleged misconduct of the tobacco industry, is more general. It can be applied to any outcome, such as mortality, morbidity, or income from excise taxes, as well as to any situation in which consequences due to alleged misconduct (for example, of two entities, such as the tobacco and the asbestos industries) or due to hypothetical programmes (for example, extra smoking reduction initiatives) are to be estimated.

Delivery of Health Care↗

PubMed1996

Causal Attributions and Reading Achievement: Individual Differences in Low-Income Families

In this study the development of causal attributions about reading within low-income families was examined. Specifically, relations between children's reading achievement and their causal attributions were investigated as well as relations between the children's attributions about themselves and their parents' attributions about them. A total 513 students from Grades 3, 6, and 9, and one parent of each student, all from low-income families, participated. Students and parents independently rated the importance of seven causal variables (effort, intellectual ability, liking for reading, the teacher, help at home, difficulty of reading material, and luck) for the students' good and poor reading outcomes. The major findings were that (a) at each grade, students' attributions were reliably related to their reading achievement on the Gates-MacGinitie reading comprehension test, with attributions to ability, liking for reading, and help at home especially critical; (b) at each grade, parent attributions were reliably associated with student attributions; and (c) as students' grade in school increased, they focused more on themselves and less on others as causal determinants of their reading performance. The implications of these findings for research and education are discussed.

Journal Article↗

PubMed1988

Causal attributions and coping with pain in chronic headache sufferers.

In the present study the relationship between attributions of causality and pain-coping behavior in headache patients was examined. Data from 441 chronic headache sufferers were collected by means of self-report inventories. The most frequently reported causal attributions were hereditary factors, emotional distress, menses or menopause, an overactive life-style, weather conditions, nutrition, and physical exertion. Some support was found for a hypothesized association between physically and psychologically related causal attributions and allied pain-coping behavior. However, as far as a relationship was revealed, it served to explain only less than 2% of the variance in pain-coping behavior. It is concluded that causal attributions do not contribute to the understanding of pain-coping behavior in chronic headache sufferers.

Adaptation, Psychological↗

PubMed2000

Information processing in large-scale cerebral networks: the causal connectivity approach.

Today, cognitive functions are considered to be the offspring of the activity of large-scale networks of functionally interconnected cerebral regions. The interpretation of cerebral activation data provided by functional imaging has therefore recently moved to the search for the effective connectivity of activated regions, which aims at understanding the role of anatomical links in the activation propagation. Our assumption is that only causal connectivity can offer a real understanding of the links between brain and mind. Causal connectivity is based on the anatomical connection pattern, the information processing within cerebral regions and the causal influences that connected regions exert on each other. In our approach, the information processing within a region is implemented by a causal network of functional primitives, which are the interpretation of integrated biological properties. Our choice of a qualitative representation of information reflects the fact that cerebral activation data are only the approximate view, provided by imaging techniques, of the real cerebral activity. This explicit modeling approach allows the formulation and the simulation of functional and physiological assumptions about activation data. Two alternative models explaining results of the striate cortex activation described by Fox and Raichle (Fox PT, Raichle ME (1984) J. Neurophysiol 51:1109-1120; Fox PT, Raichle ME (1985) Ann Neurol 17:303-305) are provided as an example of our approach.

Animals↗

PubMed2005

A new method for detecting causality in fMRI data of cognitive processing.

One of the most important achievements in understanding the brain is that the emergence of complex behavior is guided by the activity of brain networks. To fully apply this theoretical approach fully, a method is needed to extract both the location and time course of the activities from the currently employed techniques. The spatial resolution of fMRI received great attention, and various non-conventional methods of analysis have previously been proposed for the above-named purpose. Here, we briefly outline a new approach to data analysis, in order to extract both spatial and temporal activities from fMRI recordings, as well as the pattern of causality between areas. This paper presents a completely data-driven analysis method that applies both independent components analysis (ICA) and the Granger causality test (GCT), performed in two separate steps. First, ICA is used to extract the independent functional activities. Subsequently the GCT is applied to the independent component (IC) most correlated with the stimuli, to indicate its causal relation with other ICs. We therefore propose this method as a promising data-driven tool for the detection of cognitive causal relationships in neuroimaging data.

Algorithms↗

PubMed2026

Limited evidence for causal effects of circulating inflammatory cytokines on the risk of selected hematologic malignancies: a two-sample Mendelian randomization study.

BACKGROUND: Hematologic malignancies have been linked to inflammatory cytokine levels; however, whether a causal relationship exists between inflammatory cytokines and hematologic malignancies remains uncertain. This study aimed to explore the causal association between inflammatory cytokines and hematologic malignancies using Mendelian randomization (MR) analysis. METHODS: Summary statistics from genome-wide association studies of 41 inflammatory cytokines, C-reactive protein, and selected hematologic malignancies were obtained from the UK Biobank, YFS, FINRISK, and FinnGen consortia. The inverse-variance weighted (IVW) method with false discovery rate (FDR) adjustment was used as the primary MR method. The weighted median, MR-Egger regression, MR-Robust Adjusted Profile Score, and MR pleiotropy residual sum, and outlier methods were used as supplied analyses. MR-Egger intercept estimates and Cochran's Q test were used to assess pleiotropy and heterogeneity. Leave-one-out analysis and the MR Steiger test were used to assess sensitivity and the direction of causality. RESULTS: Although the primary IVW analysis indicated that some inflammatory cytokines were associated with risk of selected hematologic malignancies, no significant causal relationship between cytokines and selected hematologic malignancies was detected after FDR correction. CONCLUSION: Genetically predicted cytokine levels did not have a significant effect on the risk of the selected hematologic malignancies. Further research is warranted to confirm the potential association between cytokine levels and the risk of selected hematologic malignancies.

C-reactive protein↗

PubMedDate not supplied

Causal reasoning in computer programs for medical diagnosis.

Over the last decade substantial advances have been made in the use of causal pathophysiological knowledge in artificial intelligence-based programs for medical diagnosis. Various forms of causal representations have been used. They include probabilistic models, quantitative models, qualitative models, and models that describe causal relations at multiple levels of detail. This paper briefly analyses these methods using three representative systems. Outstanding problems and possible direction in further exploitation of causal reasoning for medical decision-support systems are also discussed.

Artificial Intelligence↗