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Results for “causality”

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PubMed2006

Reverse causality and confounding and the associations of overweight and obesity with mortality.

OBJECTIVE: To examine the effect of reverse causality and confounding on the association of BMI with all-cause and cause-specific mortality. RESEARCH METHODS AND PROCEDURES: Data from two large prospective studies were used. One (a community-based cohort) included 8327 women and 7017 men who resided in two Scottish towns at the time of the baseline assessment in 1972-1976; the other (an occupational cohort) included 4016 men working in the central belt of Scotland at the time of the baseline assessment in 1970-1973. Participants in both cohorts were ages 45 to 64 years at baseline; the follow-up period was 28 to 34 years. RESULTS: In age-adjusted analyses that did not take account of reverse causality or smoking, there was no association between being overweight (BMI 25 to <30 kg/m(2)) and mortality, and weak to modest associations between obesity (BMI > or =30 kg/m(2)) and mortality. There was a strong association between smoking and lower BMI in women and men in both cohorts (all p < 0.0001). Among never-smokers and with the first 5 years of deaths removed, overweight was associated with an increase in all-cause mortality (relative risk ranging from 1.12 to 1.38), and obesity was associated with a doubling of risk in men in both cohorts (relative risk, 2.10 and 1.96, respectively) and a 60% increase in women (relative risk, 1.56). In both never-smokers and current smokers, being overweight or obese was associated with important increases in the risk of cardiovascular disease. DISCUSSION: These findings demonstrate that with appropriate control for smoking and reverse causality, both overweight and obesity are associated with important increases in all-cause and cause-specific mortality, and in particular with cardiovascular disease mortality.

Body Mass Index↗

PubMed2026

Correlations between causal effect sizes of proximal SNPs vary with functional annotations and implicate stabilizing selection.

Causal disease effect sizes of proximal single-nucleotide polymorphisms (SNPs) are widely assumed to be independent but could be correlated. Here we introduce a new method, linkage disequilibrium SNP-pair effect correlation regression (LDSPEC), to estimate the correlation of causal disease effect sizes of derived alleles between proximal SNPs; LDSPEC produced robust estimates in simulations. Analyzing 70 UK Biobank diseases and traits (average N = 305,646), we detected significantly non-zero SNP-pair effect correlations (for example, -0.37 ± 0.09 for low-frequency positive linkage disequilibrium 0-100-bp SNP pairs) that decayed with distance and varied with allele frequency and linkage disequilibrium between SNPs. SNP pairs with shared functions had stronger effect correlations that spanned longer genomic distances. Consequently, SNP heritability estimates were smaller than estimates of the sum of causal effect size variances across SNPs, particularly for certain functional annotations. We recapitulated our findings via forward simulations involving stabilizing selection, implicating the action of linkage masking, whereby haplotypes containing linked SNPs with opposite effects on disease have reduced effects on fitness and escape negative selection.

Polymorphism, Single Nucleotide↗

PubMed2003

Most smokeless tobacco use is not a causal gateway to cigarettes: using order of product use to evaluate causation in a national US sample.

AIMS: To evaluate non-causal and causal patterns of smokeless tobacco (SLT) and cigarette use; to assess the prevalence of 'non-gateway' and possible 'gateway' patterns of SLT use. DESIGN AND SETTING: Data from the Cancer Control Supplement to the 1987 National Health Interview Survey, a representative survey of non-institutionalized adults in the United States. From reported age at first use, participants were categorized by type and sequence of tobacco product use. SUDAAN 8.0.1 was used for statistical analyses. PARTICIPANTS: Males aged 18-34 (n = 3454), weighted to provide estimates of the US population. A subsample of males aged 23-34 (n = 2614) was analyzed to minimize the possibility of future product switching. MEASUREMENTS: Smoking status, smokeless tobacco (snuff, chewing tobacco, both) use status, age at regular use of cigarettes, age at first use of smokeless tobacco. FINDINGS: Of those 23-34-year-olds who had ever used SLT with or without cigarettes, 77.2% (95% CI: 71.3, 83.3) were classifiable as non-gateway users in that 35.0% (95% CI: 29.9, 40.1) had only used SLT and 42.2% (95% CI: 36.8, 47.7) had used cigarettes first. Cigarette use in younger cohorts was less common, despite increased SLT use. Those who used cigarettes before moist snuff were 2.1 times more likely to have quit smoking (95% CI 1.21,6.39) than cigarette-only users. CONCLUSIONS: The large majority of SLT users are non-gateway users. Causal gateway effects should be of minor concern for policy. SLT may be more likely to prevent smoking than cause it.

Adolescent↗

PubMed2003

Causality assessment of adverse drug reactions: comparison of the results obtained from published decisional algorithms and from the evaluations of an expert panel, according to different levels of imputability.

OBJECTIVES: To evaluate agreement between causality assessments of reported adverse drug reactions (ADRs) obtained from decisional algorithms, with those obtained from an expert panel using the WHO global introspection method (GI), according to different levels of imputability and to evaluate the influence of confounding variables. METHOD: Two hundred reports were included in this study. An independent researcher used decisional algorithms, while an expert panel assessed the same ADR reports using the GI, both aimed at evaluating causality. Reports were divided according to the presence, absence or lack of information on confounding variables. RESULTS: The rates of concordance between assessments made using the algorithms and GI according to levels of imputability were: 45% for 'certain', 61% for 'probable', 46% for 'possible' and 17% for drug unrelated terms. When confounding variables were taken into account, the rates of concordance for the 'absence of information', 'lack of information' and 'presence of confounding variables' in the 'certain' group were 49, 69 and 7%, respectively. The corresponding values for the 'probable' group were 80, 68 and 24% and 30, 51 and 51%, respectively for the 'possible' group. CONCLUSION: Full agreement with global introspection was not found for any level of causality assessment. Confounding variables were found to be associated with low levels of agreement between decision algorithms and the GI method compromising the algorithms' sensitivity and specificity.

Adverse Drug Reaction Reporting Systems↗

PubMed2002

Behavioural knowledge, causal beliefs and self-efficacy as predictors of special educators' emotional reactions to challenging behaviours.

Theoretical models and emerging empirical data suggest that the emotional reactions of staff to challenging behaviours may affect their responses to challenging behaviours and their psychological well-being. However, there have been few studies focusing on factors related to staff emotional reactions. Seventy staff working in educational environments with children with intellectual disability and/or autism completed a self-report questionnaire that measured demographic factors, behavioural causal beliefs, behavioural knowledge, perceived self-efficacy, and emotional reactions to challenging behaviours. Regression analyses revealed that behavioural causal beliefs were a positive predictor, and self-efficacy and behavioural knowledge were negative predictors of negative emotional reactions to challenging behaviours. Staff with formal qualifications also reported more negative emotional reactions. No other demographic factors emerged as significant predictors. The results suggest that behavioural causal beliefs, low self-efficacy and low behavioural knowledge may make staff vulnerable to experiencing negative emotional reactions to challenging behaviours. Researchers and clinicians need to address these issues in staff who work with people with challenging behaviours.

Adolescent↗

PubMed1982

The perception of causality in infants.

The problem of the origins of the perception of causality in infancy has received relatively little attention in the literature despite its obvious importance. Two experiments with infants 4 1/2 and 8 months old are reported which seek to investigate sensitivity to spatiotemporal continuity in simple causal events with a differential dishabituation-of-looking technique. In the first experiment inanimate events of the familiar 'billiard-ball launching' type were used, while in the second animate events involving a hand/object pick-up were presented. The results suggest that both age groups of infants were sensitive to certain changes in spatiotemporal continuity in both types of event, although in the case of the inanimate stimuli the younger infants reacted less positively. It is suggested that infants in the first year of life are sensitive to certain spatiotemporal event configurations and that this sensitivity could be regarded as at least a required component of a perception of causality.

Age Factors↗

PubMed2006

Wild rhesus monkeys generate causal inferences about possible and impossible physical transformations in the absence of experience.

Human infants and adults generate causal inferences about the physical world from observations of single, novel events, thereby violating Hume's thesis that spatiotemporal cooccurrence from prior experience drives causal perception in our species. Is this capacity unique or shared with other animals? We address this question by presenting the results of three experiments on free-ranging rhesus monkeys (Macaca mulatta), focusing specifically on their capacity to generate expectations about the nature of completely unfamiliar physical transformations. By using an expectancy violation looking-time method, each experiment presented subjects with either physically possible or impossible transformations of objects (e.g., a knife, as opposed to a glass of water, appears to cut an apple in half). In both experiments, subjects looked longer when the transformation was impossible than when it was possible. Follow up experiments ruled out that these patterns could be explained by association. These results show that in the absence of training or direct prior experience, rhesus monkeys generate causal inferences from single, novel events, using their knowledge of the physical world to guide such expectations.

Animals↗

PubMed1995

Children's inferences of causal events during story understanding.

Second-grade children's inference of causal events was studied. Each of the 24 children heard 2 stories containing 6 event episodes. Each story was followed by either a sentence recognition or a cued-recall test. Event episodes explicitly stated an event followed by its outcome. Enablement episodes explicitly stated an action that was causally antecedent to the event, followed by the outcome of the event. Filler (control) episodes explicitly stated an action causally unrelated to the event, followed by the outcome of the event. The results indicate that the children more frequently inferred events from enablements than they inferred enablements from events, and that the children did not make the inferences at the time of reading, but instead made them when queried at the time of the test. This conclusion is suggested because there was false recognition of events but no false recall.

Attention↗

PubMed2002

A comparison of four causal factors of embarrassment in public and private situations.

In this study the authors investigated which of 4 causal factors of embarrassment is most characteristic in public and in private situations. University students (N = 288) were presented with 2 scenes representing public or private situations and were asked to rate 17 items related to the 4 factors causing embarrassment. The results indicated that in the public situation the causal factors were ordered as (a) disruption of social interaction, (b) apprehension of social evaluation, (c) inconsistency with self-image, and (d) loss of self-esteem, and in the private situation causal factors were ordered as (a) loss of self-esteem, (b) inconsistency with self-image, (c) apprehension of social evaluation, and (d) disruption of social interaction. From these results, new models for the mediating mechanism of embarrassment are proposed.

Adolescent↗

PubMed2025

Causal Inference for Genomic Data with Multiple Heterogeneous Outcomes.

With the evolution of single-cell RNA sequencing techniques into a standard approach in genomics, it has become possible to conduct cohort-level causal inferences based on single-cell-level measurements. However, the individual gene expression levels of interest are not directly observable; instead, only repeated proxy measurements from each individual's cells are available, providing a derived outcome to estimate the underlying outcome for each of many genes. In this paper, we propose a generic semiparametric inference framework for doubly robust estimation with multiple derived outcomes, which also encompasses the usual setting of multiple outcomes when the response of each unit is available. To reliably quantify the causal effects of heterogeneous outcomes, we specialize the analysis to standardized average treatment effects and quantile treatment effects. Through this, we demonstrate the use of the semiparametric inferential results for doubly robust estimators derived from both Von Mises expansions and estimating equations. A multiple testing procedure based on Gaussian multiplier bootstrap is tailored for doubly robust estimators to control the false discovery exceedance rate. Applications in single-cell CRISPR perturbation analysis and individual-level differential expression analysis demonstrate the utility of the proposed methods and offer insights into the usage of different estimands for causal inference in genomics.

Derived outcomes↗

PubMed1989

Influence of perceived competence and causal dimension orientation on expectations, persistence, and performance during perceived failure.

This investigation was conducted to compare the influence of high and low perceived competence (PC) and various causal dimension orientations on expectations, persistence, and performance while perceiving failure on a motor task. Based on a response to a PC scale, 84 junior high school students were selected to participate in this study. Separately, 42 low and 42 high PC subjects were oriented to perceive their performance on a stabilometer balancing task as due to: (a) internal, controllable, unstable factors (ICU); (b) internal, uncontrollable, stable factors (IUS); or (c) nothing in particular (NDO). Subjects then received fictitious negative feedback over 15 trials. Results revealed that persistence, expectations, and performance dependent measures were significant for PC and Causal Dimension groups. More specifically, the High PC group persisted longer, had higher expectations, and performed significantly better than the Low PC group. Furthermore, the subjects presented ICU orientations persisted for a longer amount of time, revealed higher expectations over trials, and performed significantly better over trials than the subjects in the IUS and NDO groups. These findings suggest that perceived competence and causal dimensional orientations influence children's expectations, persistence, and performance when perceiving failure on a motor task.

Achievement↗

PubMed2001

Air pollution and health: correlation or causality? The case of the relationship between exposure to particles and cardiopulmonary mortality.

Many epidemiologic studies have observed, in different contexts, a slight short-term relationship between particles in air and cardiopulmonary mortality, even when air quality standards were respected. The causality of this relationship is important to public health because of the number of people exposed. Our aim was to make a critical assessment of the arguments used in 15 reviews of published studies. We explain the importance of distinguishing validity from causality, and we systematically analyze the various criteria of judgment within the context of ecologic time studies. Our conclusion is that the observed relationship is valid and that most of the causality criteria are respected. It is hoped that the level of exposure of populations to these particles be reduced. In Europe, acting at the root of the problem, in particular on diesel emissions, will also enable the reduction of levels of other pollutants that can have an impact on health. In the United States, the situation is more complicated, as particles are mainly secondary. It is also essential to continue with research to become better acquainted with the determinants of personal global exposures and to better understand the toxic role of the various physicochemical factors of the particles.

Air Pollution↗

PubMed2006

Inferring causality assessments from predictive responses: cue interaction without cue competition.

The authors criticize the use of participants' predictive responses during a learning phase as a measure of causal assessments. Simulations demonstrate that a general aim to minimize error as required in the prediction task will produce results in accordance with those observed, in particular apparent cue interaction effects. It is argued that measures estimated from prediction responses do not reflect causal ratings, but are side-effects of learning explained by adaptation in strategy to changes in presented cell frequencies. The results are reinterpreted to be in line with a causal model view, not requiring dual processes or cue competition of lower level associative processes.

Association Learning↗

PubMed1998

Backward blocking and recovery from overshadowing in human causal judgement: the role of within-compound associations.

We replicated and extended a project by Dickinson and Burke (1996) that concerned human causal judgement. In a medical diagnostic setting, college students' ratings of the causal efficacy of target cues showed retrospective revaluation: relative to a proper control condition, ratings of target cues both increased ("recovery from overshadowing") and decreased ("backward blocking") during a second stage of training in which competing cues, but not target cues, were presented. These changes in causal judgements were exhibited only by subjects who had learned which target and competing cues were paired with one another during the first stage of training. These results cannot be explained by the Rescorla-Wagner (1972) model of associative learning, but they can be explained by the revised model of Van Hamme and Wasserman (1994); the revised model assigns non-zero salience to non-presented target stimuli whose memories or representations are retrieved by competing stimuli that had previously been paired with those target stimuli.

Adult↗

PubMed2026

Causal circuit tracing reveals distinct computational architectures in single-cell foundation models: inhibitory dominance, biological coherence, and cross-model convergence.

MOTIVATION: Sparse autoencoders (SAEs) decompose foundation-model activations into interpretable features, but the model-internal causal interactions between those features (i.e. what ablating one feature does to the others, as distinct from the biological causal structure of the underlying cells)-and how those model-internal relationships relate to biological structure-are uncharacterized in single-cell foundation models. RESULTS: We introduce model-internal causal circuit tracing-zeroing one SAE feature at a source layer and measuring the resulting change in all downstream SAE features, for each of 120 source features-and apply it to Geneformer V2-316M and scGPT whole-human across four conditions (96 892 ablation-derived edges, 80 191 forward passes). On annotation-selected source features, edges share GO/KEGG/Reactome/STRING/TRRUST ontology terms at 50.9%-68.5%, a 2.9-6.2× enrichment over a configuration-preserving permutation null (P<.002); on 20 randomly sampled source features this attenuates to 21.5%-26.3%-still 2.5-3.1× above null-quantifying the annotation-selection contribution. Inhibitory dominance (fraction of ablation edges with d<0, i.e. source activation supports downstream target) is 65.5%-89.4%. scGPT produces larger raw per-edge effects (mean |d|=1.40 versus 1.05); after feature-share normalization, Geneformer is stronger (paired gene-pair ratio 0.64 on 33 301 shared pairs). Cross-model consensus yields 1142 architecture-invariant domain pairs (ordered pairs of GO biological-process categories "A→B" each connected by at least one ablation edge in both models; 10.6× enrichment over permutation null; P<.001). Circuit edge magnitude explains <1% of the variance in marginal driver-gene coexpression on the same cells (R2=0.010, n=31 176): the graph encodes structure beyond bivariate correlation. Against a matched-cell-type ENCODE ChIP-seq prior, circuit-predicted transcription factor (TF)→target pairs are enriched 2.06× (Fisher OR 5.84), markedly higher than 1.12× against TRRUST; direct ChIP-seq-supported target pairs show 10-30× larger CRISPRi sign-bias-corrected excess than indirect pairs. Gene-level CRISPRi validation on Replogle K562 and the noncancer RPE1 arm (and a true primary-T-cell control from Shifrut E, Carnevale J, Tobin V et al. Genome-wide CRISPR screens in primary human T cells reveal key regulators of immune function. Cell 2018; 175: 1958-71.e15) after sign-bias correction shows excess over baseline of +0.03 and +0.35 percentage points on K562 and RPE1, respectively (baseline already 52%-56% from sign marginals); effect-magnitude Spearman correlations ρ≈0. Bootstrap and per-cell-type stability (N∈{50,100,200}; B cell, CD4 + T, macrophage) give Pearson r≥0.97 on shared edges with 100% sign agreement; edge Jaccard grows monotonically with sample size. The circuit graph is therefore highly reproducible as an effect-size map, cell type specific in edge identity, consistent with coexpression encoding, and weakly but detectably enriched for ChIP-seq-supported direct regulatory edges. AVAILABILITY AND IMPLEMENTATION: https://github.com/Biodyn-AI/bio-sae-circuits (Python). Archival DOI: 10.5281/zenodo.19,633,166 (Zenodo).

Humans↗

PubMed1999

Identification of causal relations between haemodynamic variables, auditory evoked potentials and isoflurane by means of fuzzy logic.

The aim of this study was to identify a possible relationship between haemodynamic variables, auditory evoked potentials (AEP) and inspired fraction of isoflurane (ISOFl). Two different models (isoflurane and mean arterial pressure) were identified using the fuzzy inductive reasoning (FIR) methodology. A fuzzy model is able to identify non-linear and linear components of a causal relationship by means of optimization of information content of available data. Nine young female patients undergoing hysterectomy under general anaesthesia were included. Mean arterial pressure (MAP), heart rate (HR), end-tidal expired carbon dioxide (CO2ET), AEP and ISOFl were monitored with a sampling time of 10 s. The AEP was extracted using an autoregressive model with exogenous input (ARX model) which decreased the processing time compared with a moving time average. The AEP was mapped into a scalar, termed the depth of anaesthesia index (DAI) normalized to 100 when the patient was awake and descending to an average of 25 during loss of consciousness. The FIR methodology identified those variables among the input variables (MAP, HR, CO2ET, DAI or ISOFl) that had the highest causal relation with the output variables (ISOFl and MAP). The variables with highest causal relation constitute the ISOFl and MAP models. The isoflurane model predicted the given anaesthetic dose with a mean error of 12.1 (SD 10.0)% and the mean arterial pressure model predicted MAP with a mean error of 8.5 (7.8)%.

Anesthetics, Inhalation↗

PubMed2026

Mapping the causal chain from genetic risk variants to lipid dysmetabolism in Parkinson's disease.

The molecular pathways linking genetic variants to Parkinson's disease (PD) onset and progression remain incompletely defined; however, risk alleles in multiple genes, including GBA1, strongly implicate lipid metabolism. To systematically identify causal biomarker signatures, we analysed comprehensive metabolome profiles from blood plasma in 149 PD patients and 150 controls, along with complementary genetic, RNA-sequencing and metabolic data from other available clinical and pathologic cohorts. Using colocalization and summary-data-based Mendelian randomization, we tested whether expression and metabolic quantitative trait loci mediate the association between implicated genetic variants and PD risk. We further integrated differential metabolomics and proteomics from blood and brain to reveal pertinent mechanisms. We show that common PD risk variants at the serine palmitoyltransferase small subunit B (SPTSSB) locus, a key regulator of de novo sphingolipid biosynthesis, are associated with increased SPTSSB brain expression and elevated plasma ceramides. Additional analyses strongly support our hypothesis that a common SPTSSB causal variant is responsible for PD risk as well as the expression and metabolic quantitative trait loci. Multiple sphingolipids and fatty acid derivatives were perturbed in PD, and we identified both unique and shared features with the Alzheimer's disease metabolome. A PD acylcarnitine signature was further replicated in human post-mortem brain tissue, when comparing those with or without preclinical Lewy body pathology. Integrated analysis of complementary brain proteomic profiles revealed dysregulation of mitochondrial processes dependent on acylcarnitines, including fatty acid beta-oxidation, the tricarboxylic acid cycle and oxidative phosphorylation. Our results identify promising biomarkers and reveal a causal chain linking genetic variation to altered gene/protein expression, lipid dysmetabolism, and the manifestation of PD.

Humans↗

PubMed1997

Causal modeling to estimate sensitivity and specificity of a test when prevalence changes.

The objective of this paper is to demonstrate, by causal modeling, whether the sensitivity and specificity of a test are constant, or whether they change with prevalence. I use three assumptions, diagnostic, predictive, and correlational, in three sets of mathematical models. A diagnostic test measures an outcome of a disease and is based on the assumption that the "gold standard" result indicates a disease state that causes a test result. A predictive test measures a risk factor for a disease and is based on the assumption that the test result indicates a risk factor that causes a disease state. A correlational test measures a condition which is an outcome of an underlying causal risk factor for a disease and is based on the assumption that the disease and the test result are noncausally related. I find that sensitivity and specificity are constant for diagnostic tests but change with prevalence for predictive and correlational tests. I present equations to show the effects on various test performance indices when prevalence changes. Different equations must be used to estimate the sensitivity, specificity, and other test performance indicators for various types of tests that are under different causal assumptions.

Diagnostic Tests, Routine↗