SEARCH · PubMed Health

Results for “causality”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 181 records · Page 10Linked to original sources

PubMed1997

Determining whether causal order affects cue selection in human contingency learning: comments on Shanks and Lopez (1996)

Shanks and Lopez (1996) reported three experiments in which they attempted to test whether causal order affects cue selection, and concluded that it does not. Their study provides an opportunity to highlight some basic methodological criteria that must be met in order to test whether and how causal order influences learning. In particular, it is necessary to (1) ensure that participants consistently interpret the learning situation in terms of directed cause-effect relations; (2) measure the causal knowledge they acquire; (3) manipulate causal order; and (4) control the statistical relations between cause and effect. With respect to these criteria, each experiment reported by Shanks and Lopez fails on multiple counts. Moreover, several aspects of the results reported by Shanks and Lopez are explained by causal-model theory, but not by associative accounts. Their study thus adds to a growing body of evidence from different laboratories indicating that human contingency learning can be guided by causal interpretation.

Association Learning↗

PubMed1979

What, when, and how about why: a longitudinal study of early expressions of causality.

Children's expressions of causality in natural discourse with adults were examined in terms of linguistic, contextual, and pragmatic influences. Specifically, the causal statements, questions, and responses to causal questions of eight 2-3-year-old children were examined in terms of developments in language content, form, and use. With respect to content, the referential and functional uses of causal expressions for both children and adults were to ongoing or imminent situations, with the speaker commenting on his or her intention to act, or requesting the listener to act. The major categories of reference in all three utterance types were negation, direction, and intention. In terms of form, there were (a) increasing use of connectives to link clauses for all the children, and (b) three main patterns of clause order differentiating among the children: cause/effect, effect/cause, and equal use of both orders. The use of expressions of causality developed in the order: child statements less than adult questions less than child responses less than child questions. The relationship of the linguistic context to these developments was found to be one of mutual influence between child and adult. The results are discussed in terms of previous hypotheses concerning (a) causal reasoning (especially those put forth by Piaget, and by Werner & Kaplan in 1963), (b) the relationship between language and conceptual development, (c) the constraints involved in different discourse situations, and (d) variation in child language.

Child Language↗

PubMed1997

Self-esteem and causal attributions.

The relationship between self-esteem and causal attributions of success and failure in achievement-related behavior was examined among undergraduate students. An integration of a self-consistency model of causal attribution and self-enhancement theory was attempted. Self-esteem and performance outcome conditions of success and failure served as independent variables. Success and failure conditions were created via feedback regarding the participants' performance on an anagram task. The participants' attributions of six causal elements (ability, effort, immediate effort, task difficulty, luck, and mood) were categorized and combined with three causal dimensions (internal-external locus, stability, and controllability), which served as dependent variables. Participants' expectations regarding performance also served as a dependent variable. The relationship between self-esteem, expectancies of success and failure, performance, and stable causality were reported. In terms of causal dimensions, internal, stable, and controllable dimensions were explained by self-enhancement.

Achievement↗

PubMed1998

Single-case causality assessment as a basis for clinical judgment.

Current methodology of therapeutic causality assessment can be traced back to four underlying paradigms: (1) empirical scientific judgment relies on experimentation (paradigm of experiment), (2) causality assessment requires repeated observations (paradigm of large numbers), (3) observed results must be compared with results of control observations (paradigm of comparison), and (4) observed objects or patients must be distributed to verum and control group by chance (paradigm of randomization). Problematic aspects of conventional methodology and the historical evolution of contemporary causality assessment illustrate the necessity and possibility of methodological alternatives. A fundamental alternative is offered by figural correspondence and figural experiments, which allow valid causality assessments in single-case situations without blinding, randomization, comparison, or large numbers of observations. The epistemological foundation of such single-case causality assessment is explained. Examples from clinical judgment are presented. Single-case causality assessment may be particularly appropriate for therapy judgment in complementary medicine.

Complementary Therapies↗

PubMed2026

The causal relationship between multiple cardiovascular diseases and glioblastoma: A Mendelian randomization study.

Observational studies suggest an association between glioblastoma (GBM) and cardiovascular diseases (CVDs), but a causal relationship remains unestablished. This study aimed to investigate the causal link between multiple CVDs and GBM risk. The inverse variance weighted method indicated that all 18 CVDs had significant causal associations with GBM (P < .05). Genetically predicted CVDs were uniformly associated with a lower risk of GBM (odds ratio < 1), identifying them as potential protective factors. Sensitivity analyses confirmed the absence of significant heterogeneity or horizontal pleiotropy, and the MR-Steiger test validated the correct causal direction. This Mendelian randomization (MR) study provides evidence that a range of CVDs are causally associated with a decreased risk of developing GBM. These findings suggest shared biological pathways and offer new insights for understanding GBM etiology. We conducted a 2-sample MR analysis using publicly available genome-wide association study data. GBM was the outcome, and 18 cardiovascular-related traits (including coronary artery disease, myocardial infarction, and venous thromboembolism) were exposures. Instrumental variables were single-nucleotide polymorphisms significantly associated with exposures (P < 5 × 10-8). The primary analysis used the inverse variance weighted method, supplemented with MR-Egger, weighted median, and weighted mode methods. Sensitivity analyses, including Cochran Q test, MR-Egger intercept test, leave-one-out analysis, and MR-Steiger directionality test, were performed to ensure robustness.

Causality↗

PubMed2026

Causal relationship between asthma and hernia risk: A Mendelian randomization study.

Epidemiological associations between asthma and various hernia subtypes have been reported, but the causality and direction remain unclear. This study employs a two‑sample Mendelian randomization (MR) approach to systematically assess the causal associations between asthma and 6 hernia subtypes. Using publicly available summary data of genome-wide association studies, asthma was selected as the exposure, and diaphragmatic hernia, umbilical hernia, femoral hernia, hiatus hernia, inguinal hernia, and ventral hernia were selected as outcomes. Instrumental variables were strictly screened (F-statistic > 10). The inverse‑variance weighted method was used as the primary analytical approach, supplemented with MR Egger and weighted median methods. Sensitivity analyses included heterogeneity tests, horizontal pleiotropy tests, Steiger directionality tests, leave‑one‑out analyses, and Radial MR. Reverse MR was performed for validation. Forward MR analyses revealed a significant positive causal effect of asthma on diaphragmatic hernia (odds ratio [OR] = 1.19, 95% confidence interval [CI]: 1.08-1.31, P < .001) and a suggestive association with umbilical hernia (OR = 1.19, 95% CI: 1.05-1.34, P = .007). The umbilical hernia association was significant only by the inverse‑variance weighted method; weighted median (P = .102) and MR-Egger (P = .210) estimates were not statistically significant, and the estimate attenuated after outlier removal (confirmatory OR = 1.13, 95% CI: 1.01-1.26, P = .028). Sensitivity analyses showed no significant heterogeneity or pleiotropy. Reverse MR did not identify significant causal effects of hernias on asthma, although power limitations for certain hernia subtypes should be considered. No significant associations were observed between asthma and the other hernia subtypes, although the null findings for femoral and ventral hernias should be interpreted with caution due to limited statistical power. This study provides genetic evidence supporting asthma as a causal risk factor for diaphragmatic hernia, with a suggestive association for umbilical hernia. The diaphragmatic hernia finding was robust across multiple sensitivity analyses, whereas the umbilical hernia association was less consistent and requires further confirmation. These findings contribute to a deeper understanding of the mechanistic links between asthma and specific hernia subtypes.

Mendelian Randomization Analysis↗

PubMed2026

Livestock Multi-Omics Integration: A Systematic Framework From Statistical Association to Causal Interpretation.

Livestock multi-omics integration is key to unraveling complex trait regulation, yet systematic, livestock-specific strategies remain scarce. This review traces the progression from single-omics accumulation to multi-dimensional integration, highlighting how large-scale genomic, epigenomic, and transcriptomic projects lay the foundation for functional dissection. We identify core impediments: extreme species diversity, marked data heterogeneity, limited sample sizes, and a pervasive reduction of multi-omics data to simplistic differential screens, resulting in low translational efficiency. We critically appraise four common pitfalls-overinterpreting correlation as causation, relegating proteomics to corroborating transcriptomics, incomplete microbiome-host integration lacking environmental context, and systematic neglect of metabolic fluxomics-and show how exposomics and fluxomics add necessary causal and dynamic dimensions. To address these, we propose a livestock-adapted three-tier analytical framework: (1) statistical association of cross-omics covariation patterns; (2) machine learning-driven feature mining and integrative modeling; and (3) causal interpretation encompassing Mendelian randomization, prior-knowledge-guided network inference, and physical causal evidence via fluxomics and metabolic control analysis. We further discuss how multimodal sequencing (single-cell, spatial, temporal) and generative AI can fundamentally mitigate heterogeneity and strengthen causal evidence. Finally, we outline future priorities in database standardization, livestock-specific benchmarking, and translational pipelines, charting a path from correlation-centric reporting to mechanistic causality and precision breeding.

Animals↗

PubMed2004

Causal illness attributions in somatoform disorders: associations with comorbidity and illness behavior.

OBJECTIVE: To compare causal illness beliefs between patients with unexplained physical symptoms and different comorbid disorders and to assess the association of causal illness beliefs with illness behavior. METHODS: We examined a sample of 233 patients attending treatment in primary care. Inclusion criteria were "unexplained physical symptoms." All patients were investigated using structured interviews and self-rating scales [Screening for Somatoform Symptoms (SOMS), Beck Depression Inventory (BDI), Beck Anxiety Inventory, and a 12-item instrument to assess causal attributions]. By means of factor analysis, the following illness attributions were considered: vulnerability to infection and environmental factors, psychological factors, organic causes including genetic and aging factors, and distress (including exhaustion and time pressure). RESULTS: Most patients reported multiple illness attributions. The more somatoform symptoms patients had, the more explanations in general they considered. Especially for vulnerability and organic illness beliefs, patients with somatoform symptoms had increased scores. Comorbidity with depression and with anxiety disorders was associated with more psychological attributions. Even when the influence of somatization, depression, and anxiety is controlled for, illness beliefs still showed associations with illness behavior. Organic causal beliefs and vulnerability attributions were associated with a need for medical diagnostic examinations, increased expression of symptoms, increased illness consequences, and bodily scanning. CONCLUSIONS: Multiple causal attributions can coexist demonstrating different associations with comorbid depression and illness behavior.

Adolescent↗

PubMed2003

Causal inference from randomized trials in social epidemiology.

Social epidemiology is the study of relations between social factors and health status in populations. Although recent decades have witnessed a rapid development of this research program in scope and sophistication, causal inference has proven to be a persistent dilemma due to the natural assignment of exposure level based on unmeasured attributes of individuals, which may lead to substantial confounding. Some optimism has been expressed about randomized social interventions as a solution to this long-standing inferential problem. We review the causal inference problem in social epidemiology, and the potential for causal inference in randomized social interventions. Using the example of a currently on-going intervention that randomly assigns families to non-poverty housing, we review the limitations to causal inference even under experimental conditions and explain which causal effects become identifiable. We note the benefit of using the randomized trial as a conceptual model, even for design and interpretation of observational studies in social epidemiology.

Asthma↗

PubMedDate not supplied

Investigations of causal pathways between PTSD and drug use disorders.

Although numerous studies have demonstrated an association between PTSD and substance use disorders, little is known about the causal nature of this relationship. In this article, we put forth and test major causal hypotheses. Specific hypotheses to be tested include self-medication of PTSD symptoms, substance users' high risk of exposure to traumatic events, and drug users' increased susceptibility to PTSD following a traumatic exposure. We also examine the possibility of an indirect pathway linking drug use disorders and PTSD via a shared vulnerability. Evidence for these causal hypotheses is evaluated using Hill's criteria for causal inference: strength, consistency, specificity, temporality, gradient, plausibility, coherence, experimental evidence, and analogy. We present data analytic strategies that exploit information about the temporal order of PTSD and drug use disorders to shed light on their causal relationship. Finally, we present findings on the PTSD/drug use disorder association from an epidemiologic study of young adults.

Adult↗

PubMed1997

What makes an analogy difficult? The effects of order and causal structure on analogical mapping.

In 4 experiments, the author tested 2 factors that affect the difficulty of analogies: order of presentation of information and causal structure. Experiments 1, 2, and 4 showed robust order effects for the positioning of sentences-sentence pairs in a variety of mapping problems. Experiments 2, 3, and 4 revealed the effects of causal structure in these analogies. Experiment 3 showed that the beneficial effects of causal structure are most marked in thematic, mapping problems presented in a casual question-answering context. Experiment 4 dealt with the interaction between order and causal structure and showed that order effects occur only in the presence of causal structure. Of all the analogy models in the literature, the incremental analogy machine is the best predictor of these results.

Causality↗

PubMed1999

Causal models in conventional and non-conventional medicines.

The author describes the possible causal models in both conventional and non-conventional therapies. Ontological determinism is used as a metaphysical assumption and linear causalism as a reference model. The linear causalism is here based on the following properties: sufficient condition, necessary condition, specificity, dose-response relationship, unidirectionality and externality. A subdivision of the therapy into two categories is then proposed: strong activity therapies, with strict causation, are close to linear causalism, and correspond to the therapeutic model of conventional medicine; weak activity therapies, with a much weaker type of causation, apparently tending towards indeterminacy, correspond to non-conventional medicines. Considering the internal state of a human being as a causal factor of therapeutic response, the difficulty in interpreting weak activity therapies is in part resolved and the differences with the strong activity therapies are also less pronounced.

Causality↗

PubMed2006

Conformity to the power PC theory of causal induction depends on the type of probe question.

P. W. Cheng's (1997) power PC theory of causal induction proposes that causal estimates are based on the power (P) of a potential cause, where P is the contingency between the cause and effect normalized by the base rate of the effect. Most previous research using a standard causal probe question has failed to support the predictions of the power PC model but recently Buehner, Cheng, and Clifford (2003) found that participants responded in terms of causal power when probed with a counterfactual test question, which they argued prompted participants to consider the base rate of the effect. However, Buehner et al. framed their counterfactual question in terms of frequency, a factor that has been demonstrated to decrease base rate neglect in judgements under uncertainty. In the experiment reported here, we sought to disentangle the influence of counterfactual and frequency framing of the probe question to determine which factor is responsible for encouraging responses in terms of causal power.

Causality↗

PubMed2026

TL-HDMR: a transfer learning framework for advancing equitable causal inference reveals metabolic signatures of stroke across multiple ancestries.

The limited genetic diversity in genome-wide association studies (GWAS) poses a significant challenge to the generalizability and equity of biomedical discoveries. Most causal inferences, particularly from high-dimensional phenomes (e.g. metabolomics), are primarily based on European populations, and their applicability to other ancestries remains uncertain. Traditional multivariable Mendelian randomization (MVMR) methods further struggle in high-dimensional and correlated settings due to collinearity and model instability. To bridge this gap, we present a two-step transfer learning framework for high-dimensional MR (TL-HDMR), designed to enhance causal exposure detection in understudied populations. Our approach leverages the Minimax Concave Penalty for asymptotically unbiased estimation amidst exposure correlations. Crucially, we introduce two novel pre-transfer procedures-HDMR.TSD for sourcing beneficial data and HDMR.PRESSO for filtering pleiotropic instruments-to ensure robust knowledge transfer. Extensive simulations demonstrated TL-HDMR's superior performance in ROC curves and mean absolute error over alternative methods. When applied to identify causal metabolites for stroke across multi-ancestry cohorts (European, East Asian, South Asian, and African), TL-HDMR successfully pinpointed both shared and ethnic-specific causal biomarkers, showcasing its unique capability for equitable causal inference. This work provides a powerful statistical tool that not only addresses critical methodological challenges but also promotes inclusivity and fairness in human health research.

Humans↗

PubMed1999

Seeking causal explanations in social epidemiology.

Social factors are associated with a wide variety of health outcomes. Social epidemiology has successfully used the traditional methods of surveillance and description to establish consistent relations between social factors and health status. Epidemiology as an etiologic science, however, has been largely ineffective in moving toward causal explanations for these observed patterns. Using the counterfactual approach to causal inference, the authors describe several fundamental problems that often arise when researchers seek to infer explanatory mechanisms from data on social factors. Contrasts that form standard causal effect estimates require implicit unobserved (counterfactual) quantities, because observational data provide only one exposure state for each individual. Although application of counterfactual arguments has successfully advanced etiologic understanding in other observational settings, the particular nature of social factors often leads to logical contradictions or misleading inferences when investigators fail to clearly articulate the counterfactual contrasts that are implied. For example, because social factors are often attributes of individuals and are components of structured social relations, random assignment is not plausible even as a hypothetical experiment, making typical epidemiologic contrasts inappropriate and the inference equivocal at best. Accordingly, more deliberate and creative approaches to causal inference in social epidemiology are required. Infectious disease epidemiology and systems analysis provide examples of approaches to causal inference that can be used when statistical mimicry of simple experimental designs is not tenable. In an era of increasing social inequality, valid approaches for the study of social factors and health are needed more urgently than ever.

Black or African American↗

PubMed2001

A psychometric experiment in causal inference to estimate evidential weights used by epidemiologists.

A psychometric experiment in causal inference was performed on 159 Australian and New Zealand epidemiologists. Subjects each decided whether to attribute causality to 12 summaries of evidence concerning a disease and a chemical exposure. The 1,748 unique summaries embodied predetermined distributions of 19 characteristics generated by computerized evidence simulation. Effects of characteristics of evidence on causal attribution were estimated from logistic regression, and interactions were identified from a regression tree analysis. Factors with the strongest influence on the odds of causal attribution were statistical significance (odds ratio = 4.5 if 0.001 < or = P < 0.05 and 7.2 if P < 0.001, vs P > or = 0.05); refutation of alternative explanations (odds ratio = 8.1 for no known confounder vs none adjusted); strength of association (odds ratio = 2.0 if 1.5 < relative risk < or = 2.0 and 3.6 if relative risk > 2.0, vs relative risk < or = 1.5); and adjunct information concerning biological, factual, and theoretical coherence. The refutation of confounding reduced the cutpoint in the regression tree for decision-making based on strength of association. The effect of the number of supportive studies reached saturation after it exceeded 12 studies. There was evidence of flawed logic in the responses concerning specificity of effects of exposure and a tendency to discount evidence if the P-value was a "near miss" (0.050 < P < 0.065). Evidential weights based on regression coefficients for causal criteria can be applied to actual scientific evidence.

Adult↗

PubMed2006

Causal models in epidemiology: past inheritance and genetic future.

The eruption of genetic research presents a tremendous opportunity to epidemiologists to improve our ability to identify causes of ill health. Epidemiologists have enthusiastically embraced the new tools of genomics and proteomics to investigate gene-environment interactions. We argue that neither the full import nor limitations of such studies can be appreciated without clarifying underlying theoretical models of interaction, etiologic fraction, and the fundamental concept of causality. We therefore explore different models of causality in the epidemiology of disease arising out of genes, environments, and the interplay between environments and genes. We begin from Rothman's "pie" model of necessary and sufficient causes, and then discuss newer approaches, which provide additional insights into multifactorial causal processes. These include directed acyclic graphs and structural equation models. Caution is urged in the application of two essential and closely related concepts found in many studies: interaction (effect modification) and the etiologic or attributable fraction. We review these concepts and present four important limitations. 1. Interaction is a fundamental characteristic of any causal process involving a series of probabilistic steps, and not a second-order phenomenon identified after first accounting for "main effects". 2. Standard methods of assessing interaction do not adequately consider the life course, and the temporal dynamics through which an individual's sufficient cause is completed. Different individuals may be at different stages of development along the path to disease, but this is not usually measurable. Thus, for example, acquired susceptibility in children can be an important source of variation. 3. A distinction must be made between individual-based and population-level models. Most epidemiologic discussions of causality fail to make this distinction. 4. At the population level, there is additional uncertainty in quantifying interaction and assigning etiologic fractions to different necessary causes because of ignorance about the components of the sufficient cause.

Causality↗

PubMed2024

The causal effect of gut microbiota on hepatic encephalopathy: a mendelian randomization analysis.

BACKGROUND: There is growing evidence for a relationship between gut microbiota and hepatic encephalopathy (HE). However, the causal nature of the relationship between gut microbiota and HE has not been thoroughly investigated. METHOD: This study utilized the large-scale genome-wide association studies (GWAS) summary statistics to evaluate the causal association between gut microbiota and HE risk. Specifically, two-sample Mendelian randomization (MR) approach was used to identify the causal microbial taxa for HE. The inverse variance weighted (IVW) method was used as the primary MR analysis. Sensitive analyses were performed to validate the robustness of the results. RESULTS: The IVW method revealed that the genus Bifidobacterium (OR = 0.363, 95% CI: 0.139-0.943, P = 0.037), the family Bifidobacteriaceae (OR = 0.359, 95% CI: 0.133-0.950, P = 0.039), and the order Bifidobacteriales (OR = 0.359, 95% CI: 0.133-0.950, P = 0.039) were negatively associated with HE. However, no causal relationship was observed among them after the Bonferroni correction test. Neither heterogeneity nor horizontal pleiotropy was found in the sensitivity analysis. CONCLUSION: Our MR study demonstrated a potential causal association between Bifidobacterium, Bifidobacteriaceae, and Bifidobacteriales and HE. This finding may provide new therapeutic targets for patients at risk of HE in the future.

Mendelian Randomization Analysis↗