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Results for “causality”

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PubMed2026

Causality between noise pollution and Alzheimer disease: A Mendelian randomization analysis.

The role of noise pollution as a risk factor for Alzheimer disease (AD) is unclear, with observational studies yielding conflicting results susceptible to confounding and reverse causality. To clarify this relationship, we performed a 2-sample Mendelian randomization (MR) study using summary statistics from large-scale genome-wide association studies of European populations. Genetically predicted daytime and evening noise exposure was used as an instrumental variable to assess a causal effect on AD risk. The primary analysis was conducted using the inverse-variance weighted method, with weighted median and MR-Egger methods as key sensitivity analyses. We assessed instrument validity and pleiotropy using the Cochran Q test, the MR-Egger intercept, and leave-one-out analysis. Our MR analysis found no evidence of a causal association between genetically predicted daytime noise (odds ratio [95% confidence interval] = 0.999 [0.993-1.006], P = .819) or evening noise (odds ratio [95% confidence interval] = 0.999 [0.993-1.005], P = .643) and the risk of AD. Sensitivity analyses were consistent, with no evidence of heterogeneity or directional pleiotropy. In conclusion, this study does not support a direct causal link between noise and AD. While our findings mitigate common observational biases, they do not preclude indirect mechanisms whereby noise may influence AD pathogenesis via established risk pathways, such as chronic sleep disruption and cardiovascular stress. Studies are needed to focus on disentangling these potential indirect effects.

Alzheimer Disease↗

PubMed1998

Understanding causal paths between mental illness and violence.

The stigma associated with mental illness is a major concern for patients, families, and providers of health services. One reason for the stigmatization of the mentally ill is the public perception that they are violent and dangerous. Although, traditionally, mental health advocates have argued against this public belief, a recent body of research evidence suggests that patients who suffer from serious mental conditions are more prone to violent behaviour than persons who are not mentally ill. It is a point of contention, however, whether the relationship between mental illness and violence is only one of association, or one of causality; that mental illness causes violence. A proven causal association between mental illness and violence will have major consequences for the mentally ill and major implications for caregivers, communities, and legislators. This paper outlines the key methodological barriers precluding casual inferences at this time. The authors suggest that a casual inference about mental illness and violence may yet be hasty. Because a premature statement advocating a causal relationship between mental illness and violence could increase stigma and have devastating effects on the mentally ill the authors urge researchers to consider the damage that may be produced as a result of poorly substantiated causal inferences.

Causality↗

PubMed2003

Causal attributions of Ghanaian industrial workers for accident occurrence: miners and non-miners perspective.

PROBLEM: Reports from the accident literature indicate that accident rates tend to vary with type of occupation. The mining industry has been recorded as the most dangerous with a high disabling injury rate. This observation has been attributed to the extremely stressful conditions under which miners work. Besides, the intimidating work environment in the mines has been insinuated to invoke a sense of helplessness, fatalism and hence defensive causal attributions for accident occurrences. METHOD: This study compared causal attributions between accident victims in Ghana's mining industry with their counterparts in textile factories. T values and Chi-square were employed to test for statistically significant differences between the two groups of accident victims. RESULTS: Findings indicate that there is no difference between the causal attributions for miners and non-miners. IMPACT ON INDUSTRY: Accident frequency and occupational type have no impact on causal attributions.

Accidents, Occupational↗

PubMed2006

The causal psycho-logic of choice.

Choices do not merely identify one option among a set of possibilities; choosing is an intervention, an action that changes the world. As a result, good decision making generally requires a model specifying how actions are causally related to outcomes. Interventions license different inferences than observations because an event whose state has been determined by intervention is not diagnostic of the normal causes of that event. We integrate these ideas into a causal framework for decision making based on causal Bayes nets theory, and suggest that deliberate decision making is based on simplified causal models and imaginary interventions. The framework is consistent with what we know so far about how people make decisions.

Bayes Theorem↗

PubMed2000

Mutability and propensity in causal selection.

We examined differences in causal ratings of 1 factor depending on the mutability (defined as the ease with which a factor can be imagined to be different) and causal propensity (defined as the likelihood that the event would occur in the presence of a factor) of another factor that conjoined to produce the event. In 3 studies, causal ratings of the target factor depended on the interaction of mutability and propensity of the other factor. When the other factor was high in mutability, ratings of the target decreased as the propensity of the contributing factor increased, but when the other was low in mutability, ratings of the target increased as the propensity of the contributing factor increased. Mediation analysis indicated that mutability and propensity affected causal ratings by determining the comparison against which the event was considered. Comparison judgments also mediated beliefs about which factor should have adjusted to the other.

Adult↗

PubMed2004

The collider principle in causal reasoning: why the Monty Hall dilemma is so hard.

The authors tested the thesis that people find the Monty Hall dilemma (MHD) hard because they fail to understand the implications of its causal structure, a collider structure in which 2 independent causal factors influence a single outcome. In 4 experiments, participants performed better in versions of the MHD involving competition, which emphasizes causality. This manipulation resulted in more correct responses to questions about the process in the MHD and a counterfactual that changed its causal structure. Correct responses to these questions were associated with solving the MHD regardless of condition. In addition, training on the collider principle transferred to a standard version of the MHD. The MHD taps a deeper question: When is knowing about one thing informative about another?

Causality↗

PubMed2005

Inference-based retrospective revaluation in human causal judgments requires knowledge of within-compound relationships.

In an allergist causal-judgment task, food compounds were followed by an allergic reaction (e.g., AB+), and then 1 cue (A) was revalued. Experiment 1, in which participants who were instructed that whatever was true about one element of a causal compound was also true of the other, showed a reverse of the standard retrospective revaluation effect. That is, ratings of B were higher when A was causal (A+) than when A was safe (A-). This effect was taken to reflect inferential reasoning, not an associative mechanism. In Experiment 2, within-compound associations were found to be necessary to produce this inference-based revaluation. Therefore, evidence that within-compound associations are necessary for retrospective revaluation is consistent with the inferential account of causal judgments.

Allergens↗

PubMed2003

From covariation to causation: a test of the assumption of causal power.

How humans infer causation from covariation has been the subject of a vigorous debate, most recently between the computational causal power account (P. W. Cheng, 1997) and associative learning theorists (e.g., K. Lober & D. R. Shanks, 2000). Whereas most researchers in the subject area agree that causal power as computed by the power PC theory offers a normative account of the inductive process. Lober and Shanks, among others, have questioned the empirical validity of the theory. This article offers a full report and additional analyses of the original study featured in Lober and Shanks's critique (M. J. Buehner & P. W. Cheng, 1997) and reports tests of Lober and Shanks's and other explanations of the pattern of causal judgments. Deviations from normativity, including the outcome-density bias, were found to be misperceptions of the input or other artifacts of the experimental procedures rather than inherent to the process of causal induction.

Analysis of Variance↗

PubMed2006

How causal knowledge affects classification: A generative theory of categorization.

Several theories have been proposed regarding how causal relations among features of objects affect how those objects are classified. The assumptions of these theories were tested in 3 experiments that manipulated the causal knowledge associated with novel categories. There were 3 results. The 1st was a multiple cause effect in which a feature's importance increases with its number of causes. The 2nd was a coherence effect in which good category members are those whose features jointly corroborate the category's causal knowledge. These 2 effects can be accounted for by assuming that good category members are those likely to be generated by a category's causal laws. The 3rd result was a primary cause effect, in which primary causes are more important to category membership. This effect can also be explained by a generative account with an additional assumption: that categories often are perceived to have hidden generative causes.

Association Learning↗

PubMed2001

Causality assessment of suspected virus transmission by human plasma products.

BACKGROUND: Causality assessment of reports on suspected virus transmission is crucial for early detection of infectious plasma products. Commonly used algorithms, such as the WHO criteria, do not meet the specific requirements for causality assessment of suspected virus transmission. STUDY DESIGN AND METHODS: A special algorithm, based on nucleic acid amplification and gene sequencing technology, effectiveness of validated virus-inactivation methods, empirical data concerning the safety record of the product, and information on batch-related infection clusters, was developed. The algorithm is focused on laboratory test results or otherwise standardized data, with few clinical data being required. To facilitate practical application, the algorithm has been converted into a graphical decision tree. RESULTS: The feasibility of the algorithm is shown by causality assessment of sample cases. Three cases are presented with the details of each case used in the 12-question checklist. The answers provided by the checklist led to the causality classification. CONCLUSION: The algorithm is a tool for evaluating reports of suspected virus transmission in a standardized manner. It thus has the potential to improve early signal detection in pharmacovigilance of plasma products by confirmation or exclusion of suspected infectivity in most cases.

Adult↗

PubMed2025

Asthma causally affects the brain cortical structure: a Mendelian randomization study.

OBJECTIVE: The potential causal relationship between asthma and brain structures remains uncertain. We performed a two-sample Mendelian randomization to investigate the causal effects of various asthma phenotypes - unspecified asthma, moderate-to-severe asthma, childhood-onset asthma, and adult-onset asthma (AOA) - on cerebral cortex structure. METHODS: We utilized phenotype data derived from genome-wide association studies (GWASs). The ENIGMA Consortium GWAS provided outcome variables for surface area (SA) and thickness across the whole brain and 34 region-specific areas of the cerebral cortex. Using the inverse variance-weighted method as our primary estimation approach, we employed several techniques, including Cochran's Q statistic, the MR-PRESSO global test, MR-Egger, and weighted median, to assess heterogeneity and pleiotropy, thereby ensuring the robustness of our findings. Additionally, we conducted enrichment analyses of gene sets with causal effects on cortical structure and applied bioinformatics techniques to construct interaction networks and identify hub nodes. RESULTS: At the global level, AOA was associated with a significant reduction in full cortical SA (β = -58.49 mm2, p = 0.017). In regional analyses, moderate-to-severe asthma exhibited a more pronounced impact on the cerebral cortex compared to other phenotypes. Enrichment analysis revealed that pathways implicated in brain morphology among asthma patients were primarily linked to immune and inflammation-driven pathways. CONCLUSIONS: Our findings provide new evidence supporting a causal relationship between asthma and alterations in cortical structure, offering potential explanations for cognitive and psychiatric impairments observed in individual post-asthma.

Humans↗

PubMed2006

Multivariate causal attribution and cost-effectiveness of a national mass media campaign in the Philippines.

Cost-effectiveness analysis is based on a simple formula. A dollar estimate of the total cost to conduct a program is divided by the number of people estimated to have been affected by it in terms of some intended outcome. The direct, total costs of most communication campaigns are usually available. Estimating the amount of effect that can be attributed to the communication alone, however is problematical in full-coverage, mass media campaigns where the randomized control group design is not feasible. Single-equation, multiple regression analysis controls for confounding variables but does not adequately address the issue of causal attribution. In this article, multivariate causal attribution (MCA) methods are applied to data from a sample survey of 1,516 married women in the Philippines to obtain a valid measure of the number of new adopters of modern contraceptives that can be causally attributed to a national mass media campaign and to calculate its cost-effectiveness. The MCA analysis uses structural equation modeling to test the causal pathways and to test for endogeneity, biprobit analysis to test for direct effects of the campaign and endogeneity, and propensity score matching to create a statistically equivalent, matched control group that approximates the results that would have been obtained from a randomized control group design. The MCA results support the conclusion that the observed, 6.4 percentage point increase in modern contraceptive use can be attributed to the national mass media campaign and to its indirect effects on attitudes toward contraceptives. This net increase represented 348,695 new adopters in the population of married women at a cost of U.S. $1.57 per new adopter.

Causality↗

PubMed2000

Launching the effect: representations of causal movements are influenced by what they lead to.

We investigated whether the representation of an observed causal movement is influenced by its observed effect. Subjects watched displays showing collisions between two objects. In this "launching event" (Michotte, 1946/1963), one of the two objects (Object A) started to move and set a second, initially stationary, object (Object B) into motion, which gave a strong impression of apparent causality. The apparent effectiveness of A's movement was manipulated by varying the velocities of A and B. When the velocity of B was higher than that of A, the effectiveness of the collision was high; when it was smaller it was low. Then, subjects were asked to reproduce the velocity of the causal movement. Reproduced velocity followed the velocity of both Object A and Object B, which supports the hypothesis that the effect of a movement is integrated with its apparent cause. However, when apparent causality was reduced by changing the direction of motion of B or by covering the point of collision, the influence of the effect on the representation of the cause persisted, suggesting that retroactive interference may account for the findings. The interference effect could not be reduced to temporal recency or spatial integration and was not obtained in the reverse temporal order (proactive interference). Rather, the two successive movements were blended in memory.

Adult↗

PubMed2004

Marginal structural models for analyzing causal effects of time-dependent treatments: an application in perinatal epidemiology.

Marginal structural models (MSMs) are causal models designed to adjust for time-dependent confounding in observational studies of time-varying treatments. MSMs are powerful tools for assessing causality with complicated, longitudinal data sets but have not been widely used by practitioners. The objective of this paper is to illustrate the fitting of an MSM for the causal effect of iron supplement use during pregnancy (time-varying treatment) on odds of anemia at delivery in the presence of time-dependent confounding. Data from pregnant women enrolled in the Iron Supplementation Study (Raleigh, North Carolina, 1997-1999) were used. The authors highlight complexities of MSMs and key issues epidemiologists should recognize before and while undertaking an analysis with these methods and show how such methods can be readily interpreted in existing software packages, including SAS and Stata. The authors emphasize that if a data set with rich information on confounders is available, MSMs can be used straightforwardly to make robust inferences about causal effects of time-dependent treatments/exposures in epidemiologic research.

Adult↗

PubMed2006

Causal inference for non-mortality outcomes in the presence of death.

Evaluation of the causal effect of a baseline exposure on a morbidity outcome at a fixed time point is often complicated when study participants die before morbidity outcomes are measured. In this setting, the causal effect is only well defined for the principal stratum of subjects who would live regardless of the exposure. Motivated by gerontologic researchers interested in understanding the causal effect of vision loss on emotional distress in a population with a high mortality rate, we investigate the effect among those who would live both with and without vision loss. Since this subpopulation is not readily identifiable from the data and vision loss is not randomized, we introduce a set of scientifically driven assumptions to identify the causal effect. Since these assumptions are not empirically verifiable, we embed our methodology within a sensitivity analysis framework. We apply our method using the first three rounds of survey data from the Salisbury Eye Evaluation, a population-based cohort study of older adults. We also present a simulation study that validates our method.

Aged↗

PubMed2000

Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men.

Standard methods for survival analysis, such as the time-dependent Cox model, may produce biased effect estimates when there exist time-dependent confounders that are themselves affected by previous treatment or exposure. Marginal structural models are a new class of causal models the parameters of which are estimated through inverse-probability-of-treatment weighting; these models allow for appropriate adjustment for confounding. We describe the marginal structural Cox proportional hazards model and use it to estimate the causal effect of zidovudine on the survival of human immunodeficiency virus-positive men participating in the Multicenter AIDS Cohort Study. In this study, CD4 lymphocyte count is both a time-dependent confounder of the causal effect of zidovudine on survival and is affected by past zidovudine treatment. The crude mortality rate ratio (95% confidence interval) for zidovudine was 3.6 (3.0-4.3), which reflects the presence of confounding. After controlling for baseline CD4 count and other baseline covariates using standard methods, the mortality rate ratio decreased to 2.3 (1.9-2.8). Using a marginal structural Cox model to control further for time-dependent confounding due to CD4 count and other time-dependent covariates, the mortality rate ratio was 0.7 (95% conservative confidence interval = 0.6-1.0). We compare marginal structural models with previously proposed causal methods.

Anti-HIV Agents↗

PubMed2000

Investigation of the causal relationship between tracheotomy and aspiration in the acute care setting.

OBJECTIVE: To investigate the causal relationship, if any, between tracheotomy and incidence of aspiration in the acute care setting. STUDY DESIGN: Prospective, consecutive. PATIENTS AND METHODS: Twenty adult patients evaluated between February 1997 and October 1999 participated. Criteria for inclusion were a dysphagia evaluation before tracheotomy, subsequent tracheotomy and placement of a tracheotomy tube, and then a repeat dysphagia evaluation after tracheotomy prior to decannulation. This permitted the causal relationship between tracheotomy and incidence of aspiration to be investigated. Differences between duration of tracheotomy placement and age were analyzed with the Student t test and for non-parametric nominal data the chi2 test was applied. RESULTS: No causal relationship between tracheotomy and aspiration was exhibited, as 19 of 20 (95%) subjects exhibited the same aspiration status before and after tracheotomy. All 12 (100%) subjects who aspirated before tracheotomy also aspirated after tracheotomy and 7 of 8 (88%) subjects who did not aspirate before tracheotomy also did not aspirate after tracheotomy (P > .05). In addition, no significant differences were observed between aspiration status and days since tracheotomy or age (P > .05). CONCLUSION: In the acute care setting, no causal relationship between tracheotomy and aspiration status was exhibited.

Adult↗

PubMedDate not supplied

Causal attributions for coronary heart disease among female cardiac patients.

PURPOSE: Beliefs about the etiology of coronary heart disease (CHD) can influence patient outcomes following an acute cardiac event. However, past research has focused predominantly on male patients. The present study investigated causal attributions and their associations with actual risk profiles in female cardiac patients. METHODS: Female cardiac patients consecutively admitted to hospital after an acute myocardial infarction (AMI) or for coronary artery bypass graft surgery (CAGS) were interviewed in hospital at 2, 4, and 12 months postdischarge. RESULTS: Among 260 women (mean age = 68.6, SD = 10.4), there was little correspondence between actual and perceived risk factors. Hypertension was least recognized: only 5% of the 180 women who had hypertension acknowledged it as a cause of their CHD. High cholesterol, obesity, and high-fat diet were also underacknowledged, with only 14%, 15%, and 17% of women with these risk factors implicating them in their CHD. A higher percentage, 44%, of smokers and 40% of women with a positive family history acknowledged these risk factors as a cause of their CHD. Women who had no idea about the cause of their CHD constituted 20%. There was little change in causal attributions over the 12-month study period and little apparent impact of cardiac rehabilitation (CR) program attendance on causal beliefs. CONCLUSIONS: Women were more likely to attribute their CHD to smoking or positive family history than to other major modifiable risk factors. The lack of correspondence between actual and perceived risk factors and the lack of impact of CR attendance on causal attributions highlight the need for personalized advice and support regarding risk factor modification.

Acute Disease↗