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PubMed2000

Causal status as a determinant of feature centrality.

One of the major problems in categorization research is the lack of systematic ways of constraining feature weights. We propose one method of operationalizing feature centrality, a causal status hypothesis which states that a cause feature is judged to be more central than its effect feature in categorization. In Experiment 1, participants learned a novel category with three characteristic features that were causally related into a single causal chain and judged the likelihood that new objects belong to the category. Likelihood ratings for items missing the most fundamental cause were lower than those for items missing the intermediate cause, which in turn were lower than those for items missing the terminal effect. The causal status effect was also obtained in goodness-of-exemplar judgments (Experiment 2) and in free-sorting tasks (Experiment 3), but it was weaker in similarity judgments than in categorization judgments (Experiment 4). Experiment 5 shows that the size of the causal status effect is moderated by plausibility of causal relations, and Experiment 6 shows that effect features can be useful in retrieving information about unknown causes. We discuss the scope of the causal status effect and its implications for categorization research.

Cognition↗

PubMed2006

Linear and nonlinear causality between signals: methods, examples and neurophysiological applications.

In this paper, we will present and review the most usual methods to detect linear and nonlinear causality between signals: linear Granger causality test (Geweke in J Am Stat Assoc 77:304-313, 1982) extended to direct causality in multivariate case (LGC), directed coherence (DCOH, Saito and Harashima in Recent advances in EEG and EMG data processing, Elsevier, Amsterdam, 1981), partial directed coherence (PDC, Sameshima and Baccala 1999) and nonlinear Granger causality test of Baek and Brock (in Working Paper University of Iowa, 1992) extended to direct causality in multivariate case (partial nonlinear Granger causality, PNGC). All these methods are tested and compared on several ARX, Poisson and nonlinear models, and on neurophysiological data (depth EEG). The results show that LGC, DCOH and PDC are not very robust in relation to nonlinear linkages but they seem to correctly find linear linkages if only the autoregressive parts are nonlinear. PNGC is extremely dependent on the choice of parameters. Moreover, LGC and PNGC may give misleading results in the case of causality on a spectral band, which is illustrated by our neurophysiological database.

Animals↗

PubMedDate not supplied

Levels of causal understanding in chimpanzees and children.

We compare three levels of causal understanding in chimpanzees and children: (1) causal reasoning, (2) labelling the components (actor, object, and instrument) of a causal sequence, and (3) choosing the correct alternative for an incomplete representation of a causal sequence. We present two tests of causal reasoning, the first requiring chimpanzees to read and use as evidence the emotional state of a conspecific. Despite registering the emotion, they failed to use it as evidence. The second test, comparing children and chimpanzees, required them to infer the location of food eaten by a trainer. Children and, to a lesser extent, chimpanzees succeeded. When given information showing the inference to be unsound--physically impossible--4-year-old children abandoned the inference but younger children and chimpanzees did not. Children and chimpanzees are both capable of labelling causal sequences and completing incomplete representations of them. The chimpanzee Sarah labelled the components of a causal sequence, and completed incomplete representations of actions involving multiple transformations. We conclude the article with a general discussion of the concept of cause, suggesting that the concept evolved far earlier in the psychological domain than in the physical.

Animals↗

PubMed2005

Multi-level analysis of causal attribution of injury to alcohol and modifying effects: Data from two international emergency room projects.

Although alcohol consumption and injury has received a great deal of attention in the literature, less is known about patient's causal attribution of the injury event to their drinking or factors which modify attribution. Hierarchical linear modeling is used to analyze the relationships of the volume of alcohol consumed prior to injury and feeling drunk at the time of the event with causal attribution, as well as the association of aggregate individual-level and socio-cultural variables on these relationships. Data analyzed are from 1955 ER patients who reported drinking prior to injury included in 35 ERs from 24 studies covering 15 countries from the combined Emergency Room Collaborative Alcohol Analysis Project (ERCAAP) and the WHO Collaborative Study on Alcohol and Injuries. Half of those patients drinking prior to injury attributed a causal association of their injury with alcohol consumption, but the rate of causal attribution varied significantly across studies. When controlling for gender and age, the volume of alcohol consumed and feeling drunk (controlling for volume) were both significantly predictive of attribution and this did not vary across studies. Those who drink at least weekly were less likely to attribute causality at a low volume level, but more likely at high volume levels than less frequent drinkers. Attribution of causality was also less likely at low volume levels in those societies with low detrimental drinking patterns, but more likely at high volume levels or when feeling drunk compared to societies with high detrimental drinking patterns. These findings have important implications for brief intervention in the ER if motivation to change drinking behavior is greater among those attributing a causal association of their drinking with injury.

Adult↗

PubMed2001

Evolutionary history versus current causal role in the definition of disorder: reply to McNally.

The harmful dysfunction (HD) analysis (Wakefield, American Psychologist 47 (1992a) 373) asserts that "disorder" means "harmful dysfunction", where "harm" is a value concept anchored in social values and "dysfunction" is a factual concept referring to failure of a mechanism to perform a natural function. Additionally, the HD analysis claims that a mechanism's natural functions are its naturally selected effects. McNally (Behaviour Research and Therapy (2000) pp. 309-314) argues to the contrary that "dysfunction" is a value concept referring to negative failures of function, that "function" refers to current causal roles and not evolutionarily designed causal roles, and that "disorder" consequently means "harmful failure of a mechanism to perform a valued current causal role." I reply by showing that McNally's proposals lack the HD analysis's power to explain common judgments about function, dysfunction, and disorder. "Dysfunction" cannot be a negative value concept because many dysfunctions are positive or neutral; "function" cannot refer to current causal roles because many current causal roles are not functions and some functions are not current causal roles; and "disorder" cannot refer to harmful failures of current causal roles because that definition allows almost any negative condition whatever to be a disorder and thus fails to explain the distinctions we make between disorder and non-disorder.

Adaptation, Psychological↗

PubMedDate not supplied

Causal relationships of processes of change and decisional balance: stage-specific models for smoking.

This study, a secondary analysis of prospective data of smokers, tested whether the causal relationships between the processes of change and decisional balance of the transtheoretical model of change (TTM) are stage-specific. It was expected that for smokers in the contemplation stage, higher levels of experiential processing cause the cons of smoking to become more important and the pros of smoking to become less important. In other words, the level of experiential process use was expected to causally influence decisional balance (pros minus cons) for people in the contemplation stage. For ex-smokers in the action stage, when the cons outweigh the pros (cons become more important while pros become less important), they should increase their behavioral process use: decisional balance was expected to causally influence use of behavioral processes. Cross-lagged panels were analyzed using structural equation modeling. Results indicate that experiential process use has causal predominance over decisional balance for smokers in the contemplation stage. For those in the action stage, however, neither decisional balance nor behavioral process had apparent causal predominance. Mean-level invariance indicates that the contemplation and action stages are different. Further analysis investigated smokers who progressed from contemplation to either preparation or action or from preparation to action. For these smokers who had progressed toward action, decisional balance did causally influence use of behavioral processes. This evidence provides support for the use of the TTM as the basis for planning interventions that target specific stage-dependent causal mechanisms.

Adult↗

PubMed2026

Causal relationships between somatic movement, brain structures, and mental well-being: A multi-stage Mendelian randomization study.

BACKGROUND: While the relationships between somatic movement, mental well-being, and brain health have been well established, the causal nature and underlying mechanisms of such associations remain incompletely understood. METHODS: By applying multi-stage Mendelian randomization to multi-source summary data derived from genome-wide association studies, we examined the causal effects of 4 somatic movement measures on 2 mental well-being indices and 13 types of brain structures, followed by testing the mediating roles of brain structures in accounting for the causal associations between somatic movement and mental well-being. RESULTS: Two-sample Mendelian randomization revealed that more physical activity was causally associated with greater mental well-being (life satisfaction and positive affect), while more sedentary behavior (longer leisure screen time and more sedentary behavior at work) with lower mental well-being. With respect to brain structures, sedentary behavior was causally linked to decreased volume, surface area, and local gyrification index in distributed cortical regions. Remarkably, decreased surface area of the piriform cortex was found to mediate the causal associations between sedentary behavior and lower mental well-being. CONCLUSIONS: Our findings not only complement and extend earlier reports on the associations of somatic movement with mental well-being and brain health by further resolving the causality but also help elucidate the neural mechanisms by which sedentary behavior adversely affects mental well-being.

Humans↗

PubMed2005

The meaning and computation of causal power: comment on Cheng (1997) and Novick and Cheng (2004).

D. Hume (1739/1987) argued that causality is not observable. P. W. Cheng (1997) claimed to present "a theoretical solution to the problem of causal induction first posed by Hume more than two and a half centuries ago" (p. 398) in the form of the power PC theory (L. R. Novick & P. W. Cheng, 2004). This theory claims that people's goal in causal induction is to estimate causal powers from observable covariation and outlines how this can be done in specific conditions. The authors first demonstrate that if the necessary assumptions were ever met, causal powers would be self-evident to a reasoner--they are either 0 or 1--making the theory unnecessary. The authors further argue that the assumptions the power PC theory requires to compute causal power are unobtainable in the real world and, furthermore, people are aware that requisite assumptions are violated. Therefore, the authors argue that people do not attempt to compute causal power.

Cognition↗

PubMed2006

The causal asymmetry.

It is hypothesized that there is a pervasive and fundamental bias in humans' understanding of physical causation: Once the roles of cause and effect are assigned to objects in interactions, people tend to overestimate the strength and importance of the causal object and underestimate that of the effect object in bringing about the outcome. This bias is termed the causal asymmetry. Evidence for this bias is reviewed in several domains, including visual impressions of causal relations, reasoning about Newton's third law in naive physics problems, concepts underlying linguistic expressions of causality, and research in causal judgment from contingency information. Although there might be an equivalent to the causal asymmetry in the domain of social causality, there are too many uncertainties in the evidence for conclusions to be drawn.

Group Processes↗

PubMed2003

Normative and descriptive accounts of the influence of power and contingency on causal judgement.

The power PC theory (Cheng, 1997) is a normative account of causal inference, which predicts that causal judgements are based on the power p of a potential cause, where p is the cause-effect contingency normalized by the base rate of the effect. In three experiments we demonstrate that both cause-effect contingency and effect base-rate independently affect estimates in causal learning tasks. In Experiment 1, causal strength judgements were directly related to power p in a task in which the effect base-rate was manipulated across two positive and two negative contingency conditions. In Experiments 2 and 3 contingency manipulations affected causal estimates in several situations in which power p was held constant, contrary to the power PC theory's predictions. This latter effect cannot be explained by participants' conflation of reliability and causal strength, as Experiment 3 demonstrated independence of causal judgements and confidence. From a descriptive point of view, the data are compatible with Pearce's (1987) model, as well as with several other judgement rules, but not with the Rescorla-Wagner (Rescorla & Wagner, 1972) or power PC models.

Adolescent↗

PubMed2026

Identification and genetic validation of potential therapeutic targets for pulmonary hypertension through multi-omics causal inference.

Pulmonary hypertension (PH) underscores the urgent need for novel therapeutic targets. This study aimed to employ a proteome-wide Mendelian randomization (MR) approach to systematically identify circulating proteins causally associated with PH, thereby providing genetically validated candidate targets for drug development. We adopted a 2-sample MR design, integrating large-scale plasma proteomic quantitative trait loci (pQTL) data (encompassing 4148 proteins) and summary statistics from a large-scale PH genome-wide association study (2047 cases, 8301 controls). Candidate targets were screened through a multilayered analytical pipeline comprising proteomic MR, transcriptomic MR, and summary-data-based Mendelian randomization. The ultimately identified MR-Identified Causal Candidate Targets (MR-ICTs) underwent rigorous Bayesian colocalization analysis, followed by biological characterization through functional enrichment analysis, single-cell transcriptomics, and phenome-wide association studies. Through robust genetic causal inference, this study provides that circulating proteins such as LYZ, GREM2, NID1, and PF4V1 play causal roles in PH pathogenesis. These findings offer a set of rigorously genetically validated, high-priority therapeutic targets for developing novel PH treatments, specifically addressing key pathological mechanisms such as innate immunity, BMP signaling pathway dysregulation, and platelet activation. Our multi-dimensional analysis ultimately identified 6 MR-ICTs causally associated with PH. Notably, the causal associations for lysozyme C (LYZ), gremlin-2 (GREM2), nidogen-1 (NID1), and platelet factor 4 variant 1 (PF4V1) were stringently validated by Bayesian colocalization analysis (posterior probability for hypothesis 4 [PPH4], indicating a shared causal variant, > 0.99). Functional enrichment analysis revealed significant involvement of these targets in immune response and TGF-β signaling pathways. Single-cell analysis further elucidated their cell-type-specific expression, with LYZ predominantly expressed in monocytes and PF4V1 almost exclusively in platelets.

Hypertension, Pulmonary↗

PubMed2026

The causal relationships and potential pathways between birth weight and cardiovascular diseases: A human genomics study.

The causal relationships and potential pathways between birth weight (BW) and various cardiovascular diseases (CVDs) remain unclear, particularly when discriminating maternal and fetal contributions of BW to CVDs. Leveraging the genome-wide association studies (GWASs) of BW (N = 321,223) and a range of CVDs (ncases = 43,676-181,522), we performed a 2-sample Mendelian randomization (MR) analysis to estimate the causal effect of BW, fetal-specific BW, and maternal-specific BW on coronary artery disease (CAD), myocardial infarction (MI), heart failure (HF), atrial fibrillation (AF), and stroke. Furthermore, we applied a stepwise MR analysis approach to assess the potential involvement of childhood body mass index (CBMI) and age at menarche (AAM) in the causal pathways from BW to CVDs, while considering adult BMI. Finally, we performed colocalization analyses to justify the different biological mechanisms of maternal-specific and fetal-specific BW. The 2-sample MR analysis revealed that genetically predicted higher BW per standard deviation (SD) was associated with a decreased risk of CAD (odds ratio [OR] = 0.804, 95% confidence interval [CI]: 0.731-0.883), MI (OR = 0.720, 95% CI: 0.638-0.814), and stroke (OR = 0.900, 95% CI: 0.823-0.985), but an increased risk of AF (OR = 1.279, 95% CI: 1.160-1.410). Similar associations were observed for fetal-specific/maternal-specific BW. The stepwise MR analysis indicated that CBMI and AAM could serve as factors linking BW/fetal-specific BW and CVDs, albeit in different roles, by displaying an indirect causal effect through adult BMI. However, for maternal-specific BW, our results failed to support a causal effect on CBMI or AAM. Colocalization analyses supported the distinct biological mechanisms for maternal-specific and fetal-specific BW by showing different causal genes. The study suggested that both fetal genotype and intrauterine environmental exposure contribute to the causal associations. Additionally, AAM and CBMI may play a role in the pathways linking BW and CVDs, though the effect was only observed for fetal-specific BW.

Humans↗

PubMed2025

Identifying independent causal cell types for human diseases and risk variants.

The SNP-heritability of human diseases is extremely enriched in candidate regulatory elements (cREs) from disease-relevant cell types. Critical next steps are to understand whether these enrichments are driven by multiple causal cell types and whether individual variants impact disease risk via a single or multiple of cell types. Here, we propose CT-FM and CT-FM-SNP, 2 methods accounting for cREs shared across cell types to identify independent sets of causal cell types for a trait and its candidate causal variants, respectively. We applied CT-FM to 63 GWAS summary statistics (average N = 417K) using 924 cRE annotations, primarily from ENCODE4. CT-FM inferred 79 sets of causal cell types, with corresponding SNP-annotations explaining 39.0 ± 1.8% of trait SNP-heritability. It identified 14 traits with independent causal cell types, uncovering previously unexplored cellular mechanisms in height, schizophrenia and autoimmune diseases. We applied CT-FM-SNP to 39 UK Biobank traits and predicted high-confidence causal cell types for 3,091 candidate causal non-coding SNPs-trait pairs. Our results suggest that most SNPs affect a phenotype via a single set of cell types, whereas pleiotropic SNPs might target different cell types depending on the phenotype context. Altogether, CT-FM and CT-FM-SNP shed light on how genetic variants act collectively and individually at the cellular level to affect disease risk.

Journal Article↗

PubMed2006

Nonlinear parametric model for Granger causality of time series.

The notion of Granger causality between two time series examines if the prediction of one series could be improved by incorporating information of the other. In particular, if the prediction error of the first time series is reduced by including measurements from the second time series, then the second time series is said to have a causal influence on the first one. We propose a radial basis function approach to nonlinear Granger causality. The proposed model is not constrained to be additive in variables from the two time series and can approximate any function of these variables, still being suitable to evaluate causality. Usefulness of this measure of causality is shown in two applications. In the first application, a physiological one, we consider time series of heart rate and blood pressure in congestive heart failure patients and patients affected by sepsis: we find that sepsis patients, unlike congestive heart failure patients, show symmetric causal relationships between the two time series. In the second application, we consider the feedback loop in a model of excitatory and inhibitory neurons: we find that in this system causality measures the combined influence of couplings and membrane time constants.

Journal Article↗

PubMedDate not supplied

Detecting blickets: how young children use information about novel causal powers in categorization and induction.

Three studies explored whether and when children could categorize objects on the basis of a novel underlying causal power. To test this we constructed a "blicket detector," a machine that lit up and played music when certain objects were placed on it. First, 2-, 3- and 4-year-old children saw that an object labeled as a "blicket" would set off the machine. In a categorization task, other objects were demonstrated on the machine. Some set it off and some did not. Children were asked to say which objects were "blickets." In an induction task, other objects were or were not labeled as "blickets." Children had to predict which objects would have the causal power to set off the machine. The causal power could conflict with perceptual properties of the object, such as color and shape. In an association task the object was associated with the machine's lighting up but did not cause it to light up. Even the youngest children sometimes used the causal power to determine the object's name rather than using its perceptual properties and sometimes used the object's name rather than its perceptual properties to predict the object's causal powers. Children rarely categorized the object on the basis of the associated event. Young children also sometimes made interesting memory errors-they incorrectly reported that objects with the same perceptual features had had the same causal power. These studies demonstrate that even very young children will easily and swiftly learn about a new causal power of an object and spontaneously use that information in classifying and naming the object.

Age Factors↗

PubMed2002

Principal stratification in causal inference.

Many scientific problems require that treatment comparisons be adjusted for posttreatment variables, but the estimands underlying standard methods are not causal effects. To address this deficiency, we propose a general framework for comparing treatments adjusting for posttreatment variables that yields principal effects based on principal stratification. Principal stratification with respect to a posttreatment variable is a cross-classification of subjects defined by the joint potential values of that posttreatment variable tinder each of the treatments being compared. Principal effects are causal effects within a principal stratum. The key property of principal strata is that they are not affected by treatment assignment and therefore can be used just as any pretreatment covariate. such as age category. As a result, the central property of our principal effects is that they are always causal effects and do not suffer from the complications of standard posttreatment-adjusted estimands. We discuss briefly that such principal causal effects are the link between three recent applications with adjustment for posttreatment variables: (i) treatment noncompliance, (ii) missing outcomes (dropout) following treatment noncompliance. and (iii) censoring by death. We then attack the problem of surrogate or biomarker endpoints, where we show, using principal causal effects, that all current definitions of surrogacy, even when perfectly true, do not generally have the desired interpretation as causal effects of treatment on outcome. We go on to forrmulate estimands based on principal stratification and principal causal effects and show their superiority.

Child↗

PubMed2006

Speed kills? Speed, accuracy, encapsulations and causal understanding.

BACKGROUND: The role of basic science, which provides causal explanations for clinical phenomena in medical education, is poorly understood. Schmidt has postulated that expert clinicians maintain this knowledge in 'encapsulated' form, indexed by words or phrases describing the processes. In the present paper we show that students who learn causal explanations have a more coherent understanding of the relation between diseases and clinical features which, in turn, influences recognition of words or phrases describing 'encapsulated knowledge' and the ability to maintain performance under speeded conditions. HYPOTHESES: In comparison to students who simply learn the features of 4 diagnostic categories, students who learn a causal explanation will: (a) recognise words describing encapsulated knowledge more accurately and (b) maintain or improve diagnostic performance under speeded conditions. METHODS: Two studies were conducted involving 4 'pseudo-endocrinology' diseases and undergraduate psychology students. One group learned signs and symptoms alone; the second group also learned a causal explanation. In study 1, they were then given a recognition memory task. In study 2, they were asked to diagnose new cases either (i) as quickly as possible or (ii) taking their time. RESULTS: In study 1, while there was no difference in recognising old words (90% versus 91%), the causal group was better able to recognise encapsulated and novel consistent words (50% versus 41%) (P = 0.02). In study 2 there was an interaction; causal students performed better under speeded conditions (71% versus 66%) but worse under thoroughness conditions (67% versus 73%), as predicted. CONCLUSIONS: Causal understanding leads to more coherent understanding of clinical conditions, which in turn leads to expert-like behaviour.

Clinical Competence↗

PubMed1998

A multi-level analysis of cultural experience and gender influences on causal attributions to perceived performance in mathematics.

BACKGROUND: Causal attributions to academic performance are among the most important factors that influence the student's subsequent achievement behaviour. AIM: The effects of student's gender and cultural experience (region) on the ratings of previously identified causal attribution factors were investigated. SAMPLE: The participants were 341 high school students from the urban (N = 144) and the rural (N = 197) regions of Kenya. There were 205 male and 136 female students. METHODS: Causal comparative research design was used and data collected using the Causal Attribution Scale (CAS). The Hierarchical linear model (HLM) technique was used to test the hypotheses. RESULTS: There were significant gender and cultural experience variations in the mean ratings of the attribution factors. Instructional Strategy was highly rated for perceived success, and lack of Ability for perceived failure. Effort was of least importance in making attribution to either perceived success or failure. CONCLUSIONS: The findings do not concur with research findings from Western and Asian countries where Effort is considered important in making attributions for either perceived success or failure. The findings, however, agree with research findings from Asian and other non-Caucasian societies where success is attributed to external factors (e.g., task difficulty) and failure to internal factors (e.g., ability). These findings have some implications for cross-cultural research in causal attribution as it relates to academic performance. While certain causal attributions studied in 'Western' and Asian cultures differ in terms of perceived success and failure, the current study indicates that other causal attributions are important and used differently by students from Kenya.

Achievement↗