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Results for “causality”

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PubMed1996

Within-compound associations mediate the retrospective revaluation of causality judgements.

The role of within-compound associations in the retrospective revaluation of causality judgements was investigated in a two-stage procedure in which the subjects were asked to learn whether or not different food stimuli caused an allergic reaction in hypothetical patients. In the compound-cue stage a number of compound cues, each consisting of a competing stimulus and a target stimulus, were associated with the reaction across a series of trials, whereas in the single-cue stage the subjects had the opportunity to learn which of the competing cues, when presented alone, caused the reaction. Each target stimulus was presented with the same competing cue across all compound trials in the consistent condition, but with a different competing cue on each trial in the varied condition. In a forward procedure, in which the single-cue stage preceded compound cue training, judgements of the causal effectiveness of the target stimuli were reduced or blocked by training them in compound with a competing cue that had been previously paired with the reaction. Moreover, the magnitude of this reduction was comparable in the consistent and varied conditions. This was not true, however, when the single- and compound-cue stages were reversed in the backward procedure. Judgements for target cues compounded with competing cues that were subsequently paired with the reaction were reduced only in the consistent condition. If it is assumed that stronger associations were formed between the competing and target stimuli during the compound-cue stage in the consistent condition than in the varied condition, this pattern suggests that the retrospective revaluation of causality judgements can be mediated by the formation of within-compound associations.

Allergens↗

PubMed2005

A causal inference approach for constructing transcriptional regulatory networks.

MOTIVATION: Transcriptional regulatory networks specify the interactions among regulatory genes and between regulatory genes and their target genes. Discovering transcriptional regulatory networks helps us to understand the underlying mechanism of complex cellular processes and responses. METHOD: This paper describes a causal inference approach for constructing transcriptional regulatory networks using gene expression data, promoter sequences and information on transcription factor (TF) binding sites. The method first identifies active TFs in each individual experiment using a feature selection approach. TFs are viewed as "treatments" and gene expression levels as "responses". For every TF and gene pair, a marginal structural model is built to estimate the causal effect of the TF on the expression level of the gene. The model parameters can be estimated using the G-computation procedure or the IPTW estimator. The P-value associated with the causal parameter in each of these models is used to measure how strongly a TF regulates a gene. These results are further used to infer the overall regulatory network structures. RESULTS: Our analysis of yeast data suggests that the method is capable of identifying significant transcriptional regulatory interactions and the corresponding regulatory networks. AVAILABILITY: The software is under development.

Algorithms↗

PubMed2002

An overview of relations among causal modelling methods.

This paper provides a brief overview to four major types of causal models for health-sciences research: Graphical models (causal diagrams), potential-outcome (counterfactual) models, sufficient-component cause models, and structural-equations models. The paper focuses on the logical connections among the different types of models and on the different strengths of each approach. Graphical models can illustrate qualitative population assumptions and sources of bias not easily seen with other approaches; sufficient-component cause models can illustrate specific hypotheses about mechanisms of action; and potential-outcome and structural-equations models provide a basis for quantitative analysis of effects. The different approaches provide complementary perspectives, and can be employed together to improve causal interpretations of conventional statistical results.

Causality↗

PubMed1996

Causal analysis: theory and application.

Identified opportunities to make statistical analysis more closely approximate the conceptual issues that are apparent in current research in pediatric psychology. The paper proceeds by explicating the logic of multivariate analysis and its application to causal modeling. Causal modeling techniques are discussed and the ease of employing these techniques is evaluated. Several articles from earlier issues of this journal are reviewed in terms of the application of causal analysis.

Causality↗

PubMed1991

On the origin of Hill's causal criteria.

The rules to assess causation formulated by the eighteenth century Scottish philosopher David Hume are compared to Sir Austin Bradford Hill's causal criteria. The strength of the analogy between Hume's rules and Hill's causal criteria suggests that, irrespective of whether Hume's work was known to Hill or Hill's predecessors, Hume's thinking expresses a point of view still widely shared by contemporary epidemiologists. The lack of systematic experimental proof to causal inferences in epidemiology may explain the analogy of Hume's and Hill's, as opposed to Popper's, logic.

Causality↗

PubMed1995

Causal inference in infectious diseases.

Since the 1970s, Rubin has promoted a model for causal inference based on the potential outcomes if individuals received each of the treatments under study. Commonly, the assumption is made that the outcome in one individual is independent of the treatment assignment and outcome in other individuals. In infectious diseases, however, whether one person become infected is quite often dependent on the infection outcome in other individuals, a situation known as dependent happenings. Here, we review the model proposed by Rubin for the example of infectious disease. Consequences of the violation of the stability assumption include the need for an expanded representation of outcomes, and the existence of different kinds of effects, such as direct and indirect effects. Effects of interest include changes in susceptibility as well as changes in infectiousness. We define the transmission probability formally as an average causal parameter of effect in a population by conditioning on exposure to infection. Unconditional indirect and total effects are difficult to define formally using this model for causal inference. The assignment mechanism can influence the sampling mechanism when it determines who is exposed to infection, raising problems that require further inquiry. We conclude by contrasting the role of differential exposure to infection in direct and indirect effects.

Causality↗

PubMed2003

Is the risk of adult coeliac disease causally related to cigarette exposure?

OBJECTIVE: Previous studies have shown an association between cigarette smoking and coeliac disease, but it has yet to be established whether this relationship is causal. The aim of this study was to assess causality using the Bradford Hill criteria. METHODS: A matched case-control study using a questionnaire to establish a detailed smoking history for 138 incident cases of adult coeliac disease and 276 age-matched and sex-matched controls. Subjects were categorized according to their active cigarette exposure prior to diagnosis of the matched case, and odds ratios and tests for linear trends were calculated. RESULTS: At the time of diagnosis, 10% of cases and 30% of controls were current smokers (odds ratio, 0.21 and 95% confidence interval, 0.11-0.40 for coeliac disease in current smokers versus never smokers). A biological gradient was demonstrated for total, recent and current cigarette exposure. The greatest risk reduction related to current exposure (odds ratio, 0.15, and 95% confidence interval, 0.06-0.37 for coeliac disease in current heavy smokers versus never smokers). CONCLUSIONS: This study strengthens the case for a causal relationship between smoking and coeliac disease by demonstrating a strong, temporally appropriate and dose-dependent effect, thus meeting the Bradford Hill criteria. This suggests that cigarette smoking truly protects against the development of adult coeliac disease.

Adult↗

PubMed2004

Causal analysis of individual change using the difference score.

Causal analysis of change in time-related characteristics such as health or disease is an increasingly important area of epidemiology. Change is often analyzed using data from 2 waves of a longitudinal study, using the difference score--the difference between the scores at the 2 waves--as the outcome in a regression model. In this article, I show how and when causal analysis of change can be performed using simple linear regression models of continuous difference scores. Not only do causal analyses require making adjustments for confounding bias, but also for the shape of individual "growth curves"--the way in which each individual's score changes over time. In practice, the type of growth curve is critical to determining whether age or start score or neither is included in the regression model. For valid analyses, both sets of adjustments require assumptions based on prior theory that cannot be tested using the study data; choosing to make adjustments using variables based solely on observed associations with the difference score can give misleading results. However, analysts can state their assumptions clearly using this framework and put them up for rigorous scientific scrutiny. The approach is illustrated by an application to data from the Whitehall II study of British civil servants.

Age Factors↗

PubMed2026

Causality between genetically predicted type 2 diabetes and ankle fracture risk: A 2-sample Mendelian randomization study.

It has been proven that diabetes mellitus plays an important role in the occurrence and development of joint fractures. In this study, a 2-sample Mendelian randomization (MR) analysis was conducted to investigate the causal relationship between diabetes and ankle fractures. We pooled the data from the published genome-wide association studies. Diabetes mellitus type 2 was derived from pooled genome-wide association study data of 655,666 European individuals (61,714 patients and 1178 controls). Data on ankle fractures were derived from pooled genome-wide association study data in a total of 460,340 European individuals (6479 patients and 453,861 controls). Using diabetes-associated loci as instrumental variables, we used inverse variance weighting, MR-Egger, weighted median, simple multivariate analysis and weighted multivariate analysis to evaluate the association between diabetes and ankle fracture risk. Reverse MR analysis was performed on the Diabetes mellitus type 2 that were found to be causally associated with ankle fractures in forward MR analysis. Sensitivity analysis was used to evaluate the robustness of the results. Statistical analysis showed a significant causal relationship between diabetes and ankle fractures (inverse variance weighting: OR = 1.07, 95% CI = 1.01-1.32, P = .02). Diabetes mellitus is associated with an increased risk of ankle fracture. The results of MR analysis can be used as a guide for the screening of diabetes and ankle fractures, which is helpful to improve the awareness of screening, early diagnosis and early treatment.

Humans↗

PubMed2026

Causal determinants of gout in 614,000 adults: A two-sample Mendelian randomization study of dietary, lifestyle, and metabolic traits.

Gout affects over 55 million people worldwide, with prevalence projected to rise by 70% by 2050. Although observational studies have implicated several dietary, lifestyle, and metabolic risk factors, causal relationships remain uncertain because of confounding and reverse causation. Univariable and multivariable two-sample Mendelian randomization (MR) analyses were performed using genome-wide association data from 2 European cohorts: UK Biobank (6543 self-reported gout cases and 456,390 controls) and FinnGen (3576 cases and 147,221 controls). Genetic instruments for 12 exposures, including dried fruit, salad, and cheese intake; smoking initiation; physical activity; body mass index (BMI); and lipid traits, were derived from established genome-wide association study datasets. Inverse-variance weighted regression with multiplicative random effects was the primary analysis, complemented by weighted median, weighted mode, MR-Egger, MR-Pleiotropy RESidual Sum and Outlier, and 3 predefined multivariable models. Benjamini-Hochberg correction was applied across all 24 tests. BMI showed the strongest and most consistent causal effect on gout (FinnGen: odds ratio [OR] = 1.97, 95% confidence interval [CI] = 1.51-2.57; UK Biobank: OR = 1.006, 95% CI = 1.003-1.009; both P < .001) and remained significant in 5 of 6 multivariable models. Triglycerides increased risk in both cohorts (FinnGen: OR = 1.34, 95% CI = 1.08-1.66; UK Biobank: OR = 1.008, 95% CI = 1.004-1.012). These associations survived Benjamini-Hochberg correction, as did high-density lipoprotein cholesterol in UK Biobank (OR = 0.997, 95% CI = 0.994-0.999). Dried fruit intake was inversely associated with gout in FinnGen (OR = 0.34, 95% CI = 0.13-0.92, P = .034) but not in UK Biobank, and did not survive correction for multiple testing. No other exposure reached significance in either cohort. This study provides genetic evidence that BMI is the dominant modifiable causal determinant of gout, with triglycerides contributing independently. Dietary associations were weaker and did not withstand correction for multiple testing, and should be regarded as hypothesis-generating. These findings support prioritizing weight management and metabolic health in gout prevention.

Gout↗

PubMed2025

CAUSAL ASSOCIATION BETWEEN SEPSIS AND FIBROBLAST GROWTH FACTORS AS WELL AS THEIR RECEPTORS LEVELS: A TWO-SAMPLE MENDELIAN RANDOMIZATION STUDY.

Objective: The potential association between sepsis risk and circulating levels of fibroblast growth factors (FGFs) and their receptors (FGFRs) has been a focus of research; however, the causal relationship between them remains to be elucidated. We hypothesize a causal association between genetically predicted FGFs, FGFRs, and sepsis risk, and we conduct a Mendelian randomization (MR) study to validate this hypothesis. Methods: We utilized a two-sample MR design to assess the effect of genetic variants associated with various FGFs (FGF1, FGF2, FGF7, FGF16, FGF19, FGF21, FGF23, FGF5) and FGFRs (FGFR1, FGFR2, FGFR3, α-Klotho) on sepsis risk, using genome-wide association study summary statistics. Our MR analyses employed the inverse-variance weighted (IVW) method, along with weighted median, weighted mode, and MR-Egger regression, supplemented by sensitivity analyses to ensure robustness. Results: The MR analysis identified an unequal number of instrumental variables ranging from 2 to 17 for FGFs and FGFRs when sepsis was the outcome. No significant correlation was found between genetically determined FGF levels and sepsis risk by IVW analysis (all P > 0.05). Correspondingly, similar nonsignificant associations were observed for FGFRs (all P > 0.05). Other MR methods corroborated the IVW findings. Sensitivity analyses, including Cochran's Q test, MR-Egger, and MR pleiotropy residual sum and outlier, indicated no significant heterogeneity or pleiotropy in the relationships, with the exception of a nonsignificant correlation between FGFR1 and sepsis that persisted after the exclusion of an outlier (odds ratio, 0.84; P = 0.34). Conclusion: The analysis found no significant causal associations between FGFs, their receptors, and sepsis risk, indicating a need for further research on their complex interactions.

Humans↗

PubMed2003

Causal belief and the origins of technology.

The primary function of the brain is to control movement. Human interactions with the environment, unlike those of other primates, are based on a belief in cause and effect, and this led to technology. Experiments requiring simple manipulations of the environment show that chimpanzees do not have concepts of causes or forces. Children, by contrast, have causal beliefs as a developmental primitive, and these can be demonstrated even in infants. It is proposed that the evolution of causal thinking was essential for the development of tool use, as it is not possible to make a complex tool without understanding cause and effect. This was a great evolutionary adaptive advantage. The evolution of language may have been linked to the same process. It has been technology that resulted from causal beliefs, not social interaction, that has driven human evolution.

Adaptation, Physiological↗

PubMed2000

Philosophical assumptions in Freud, Jung and Bion: questions of causality.

The historical development of concepts of causality in philosophy is described. Since the Enlightenment and the growth of science, exponents of the two most important concepts, determinism and teleology, have been in conflict. At the inception of psychoanalysis at the end of the nineteenth century this conflict was particularly intense. It was the cause of the first major schism in psychoanalysis between Jung and Freud. Psychoanalytic theorists have continued to disagree over this issue. Post-modernist philosophy has abolished all metaphysics and therefore called into question concepts of psychic causality. Parallel to, but uninfluenced by this development, Bion has developed a psychoanalytic conceptualization which may be seen as transcending causality. The clinical and theoretical implications of these developments are described.

Causality↗

PubMed2003

Using sex differences in psychopathology to study causal mechanisms: unifying issues and research strategies.

BACKGROUND: Although there is an extensive literature, both speculative and empirical, on postulated differences between males and females in their rates of particular types of disorder, very little is known about the mechanisms that underlie these sex differences. The study of mechanisms is important because it may provide clues on aetiological processes. The review seeks to outline what is known, what are the methodological hazards that must be dealt with, and the research strategies that may be employed. METHODS: We note the need for representative general samples, and for adequate measurement and significance testing if valid conclusions are to be drawn. We put forward three levels of causes that have to be considered: a genetically determined distal basic starting point; the varied consequences of being male or female; and the proximal risk or protective factors that are more directly implicated in the causal mechanisms that predispose to psychopathology. In delineating these, we argue that three key sets of evidential criteria have to be met: a) that the risk factors differ between males and females; b) that they provide for risk or protection within each sex; and c) that when introduced into a causal model, they eliminate or reduce the sex differences in the disorders being studied. RESULTS: A male excess mainly applies to early onset disorders that involve some kind of neurodevelopmental impairment. A female excess mainly applies to adolescent-onset emotional disorders. No variables have yet met all the necessary criteria but some good leads are available. The possible research strategies that may be employed are reviewed. CONCLUSIONS: The systematic investigation of sex differences constitutes an invaluable tool for the study of the causal processes concerned with psychopathology.

Adolescent↗

PubMed1996

Causality, menopause, and depression: a critical review of the literature.

OBJECTIVE: To assess whether causal criteria can be used to find out whether there is support in published research for maintaining that menopause causes depression. DESIGN: Ninety four articles from 30 years of research examining the relation of natural menopause to depression were traced by using Medline and systematic follow up of reference lists. Specified exclusion and inclusion criteria were applied, and the resulting 43 epidemiological primary research articles were classified and tabulated according to sample and measures used and the researchers' own conclusion as to whether or not an association had been established. This material was qualitatively evaluated with Hill's nine criteria for causality. RESULT: There is insufficient evidence at present to maintain that menopause causes depression. In addition to methodological and statistical problems, a temporal problem in the menopause concept hinders research in this area. CONCLUSION: Causal criteria can usefully be used to structure a literature review. Further theoretical work is required to integrate standard clinical epidemiological concepts.

Causality↗

PubMed2006

Estimating causal effects from epidemiological data.

In ideal randomised experiments, association is causation: association measures can be interpreted as effect measures because randomisation ensures that the exposed and the unexposed are exchangeable. On the other hand, in observational studies, association is not generally causation: association measures cannot be interpreted as effect measures because the exposed and the unexposed are not generally exchangeable. However, observational research is often the only alternative for causal inference. This article reviews a condition that permits the estimation of causal effects from observational data, and two methods -- standardisation and inverse probability weighting -- to estimate population causal effects under that condition. For simplicity, the main description is restricted to dichotomous variables and assumes that no random error attributable to sampling variability exists. The appendix provides a generalisation of inverse probability weighting.

Causality↗

PubMed2024

Causal Mediation Analysis for Integrating Exposure, Genomic, and Phenotype Data.

Causal mediation analysis provides an attractive framework for integrating diverse types of exposure, genomic, and phenotype data. Recently, this field has seen a surge of interest, largely driven by the increasing need for causal mediation analyses in health and social sciences. This article aims to provide a review of recent developments in mediation analysis, encompassing mediation analysis of a single mediator and a large number of mediators, as well as mediation analysis with multiple exposures and mediators. Our review focuses on the recent advancements in statistical inference for causal mediation analysis, especially in the context of high-dimensional mediation analysis. We delve into the complexities of testing mediation effects, especially addressing the challenge of testing a large number of composite null hypotheses. Through extensive simulation studies, we compare the existing methods across a range of scenarios. We also include an analysis of data from the Normative Aging Study, which examines DNA methylation CpG sites as potential mediators of the effect of smoking status on lung function. We discuss the pros and cons of these methods and future research directions.

causal inference↗

PubMed2000

Causal effects in clinical and epidemiological studies via potential outcomes: concepts and analytical approaches.

A central problem in public health studies is how to make inferences about the causal effects of treatments or agents. In this article we review an approach to making such inferences via potential outcomes. In this approach, the causal effect is defined as a comparison of results from two or more alternative treatments, with only one of the results actually observed. We discuss the application of this approach to a number of data collection designs and associated problems commonly encountered in clinical research and epidemiology. Topics considered include the fundamental role of the assignment mechanism, in particular the importance of randomization as an unconfounded method of assignment; randomization-based and model-based methods of statistical inference for causal effects; methods for handling noncompliance and missing data; and methods for limiting bias in the analysis of observational data, including propensity score matching and sensitivity analysis.

Analysis of Variance↗