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PubMed2004

Feature inference and the causal structure of categories.

The purpose of this article was to establish how theoretical category knowledge-specifically, knowledge of the causal relations that link the features of categories-supports the ability to infer the presence of unobserved features. Our experiments were designed to test proposals that causal knowledge is represented psychologically as Bayesian networks. In five experiments we found that Bayes' nets generally predicted participants' feature inferences quite well. However, we also observed a pervasive violation of one of the defining principles of Bayes' nets-the causal Markov condition-because the presence of characteristic features invariably led participants to infer yet another characteristic feature. We argue that this effect arises from a domain-general bias to assume the presence of underlying mechanisms associated with the category. Specifically, people take an exemplar to be a "well functioning" category member when it has most or all of the category's characteristic features, and thus are likely to infer a characteristic value on an unobserved dimension.

Analysis of Variance↗

PubMed2025

Causal association between 91 circulating inflammatory proteins and primary open-angle glaucoma: a bidirectional Mendelian randomization study.

BACKGROUND: Glaucoma, especially primary open-angle glaucoma (POAG), is a leading cause of irreversible vision loss. While elevated intraocular pressure is a major risk factor, the pathogenesis of POAG also involves genetics, oxidative stress, abnormal hemodynamics, and inflammatory factors. The role of systemic inflammation in POAG remains a subject of debate. This study aimed to investigate the causal relationships between circulating inflammatory proteins and POAG using a bidirectional Mendelian randomization (MR) approach. METHODS: A bidirectional two-sample MR analysis was conducted using genome-wide association study summary statistics. The primary stage involved 91 circulating inflammatory proteins and POAG, followed by a replication stage to verify significant findings using independent data and meta-analysis. The random-effects inverse-variance weighted model was employed as the primary method, complemented by multiple sensitivity analyses employed to ensure robustness, including multivariable MR to adjust for potential confounders. RESULTS: In the primary stage, 9 circulating inflammatory proteins were found to have significant causal effects on POAG. Specifically, the higher levels of Delta and Notch-like epidermal growth factor-related receptor (DNER) (OR: 1.12, 95 % CI: 1.04-1.21, P = 0.004), leukemia inhibitory factor (LIF) (OR: 1.20, 95 % CI: 1.06-1.36, P = 0.003), matrix metalloproteinase-10 (MMP-10) (OR: 1.08, 95 % CI: 1.02-1.16, P = 0.013), and stem cell factor (SCF) (OR: 1.09, 95 % CI: 1.03-1.15, P = 0.005) were positively associated with the risk of POAG. Conversely, the levels of fibroblast growth factor 19 (FGF-19) (OR: 0.88, 95 % CI: 0.82-0.95, P = 0.002), interleukin-18 (IL-18) (OR: 0.92, 95 % CI: 0.86-0.99, P = 0.019), IL-18 receptor 1 (IL-18R1) (OR: 0.96, 95 % CI: 0.92-1.00, P = 0.037), tumor necrosis factor ligand superfamily member 14 (TNFSF14) (OR: 0.91, 95 % CI: 0.86-0.97, P = 0.004), and tumor necrosis factor-related activation-induced cytokine (TRANCE) (OR: 0.94, 95 % CI: 0.88-1.00, P = 0.041) exhibited inverse associations with the risk of POAG. Multivariable MR analysis adjusting for confounders supported the roles of DNER, FGF-19, IL-18, IL18R1, LIF, and SCF. The replication stage confirmed the significant associations for FGF-19 (OR: 0.89, 95 % CI: 0.84-0.95, P = 4.63 × 10-4), IL-18 (OR: 0.93, 95 % CI: 0.89-0.97, P = 0.002), IL-18R1 (OR: 0.96, 95 % CI: 0.93-0.99, P = 0.023), and LIF (OR: 1.18, 95 % CI: 1.04-1.34, P = 0.013). Sensitivity analyses further supported the robustness of these findings. CONCLUSION: This study elucidated the causal relationships between circulating inflammatory proteins and POAG, highlighting FGF-19, IL-18, IL-18R1, and LIF as potential therapeutic targets. These findings provide new insights for the prevention and management of POAG, although further studies are needed to understand the precise biological mechanisms.

Humans↗

PubMed2003

Investigating directed cortical interactions in time-resolved fMRI data using vector autoregressive modeling and Granger causality mapping.

We present a framework aimed to reveal directed interactions of activated brain areas using time-resolved fMRI and vector autoregressive (VAR) modeling in the context of Granger causality. After describing the underlying mathematical concepts, we present simulations helping to characterize the conditions under which VAR modeling and Granger causality can reveal directed interactions from fluctuations in BOLD-like signal time courses. We apply the proposed approach to a dynamic sensorimotor mapping paradigm. In an event-related fMRI experiment, subjects performed a visuomotor mapping task for which the mapping of two stimuli ("faces" vs "houses") to two responses ("left" or "right") alternated periodically between the two possible mappings. Besides expected activity in sensory and motor areas, a fronto-parietal network was found to be active during presentation of a cue indicating a change in the stimulus-response (S-R) mapping. The observed network includes the superior parietal lobule and premotor areas. These areas might be involved in setting up and maintaining stimulus-response associations. The Granger causality analysis revealed a directed influence exerted by the left lateral prefrontal cortex and premotor areas on the left posterior parietal cortex.

Brain Mapping↗

PubMed2004

The (mis)estimation of neighborhood effects: causal inference for a practicable social epidemiology.

The resurgence of interest in the effect of neighborhood contexts on health outcomes, motivated by advances in social epidemiology, multilevel theories and sophisticated statistical models, too often fails to confront the enormous methodological problems associated with causal inference. This paper employs the counterfactual causal framework to illuminate fundamental obstacles in the identification, explanation, and usefulness of multilevel neighborhood effect studies. We show that identifying useful independent neighborhood effect parameters, as currently conceptualized with observational data, to be impossible. Along with the development of a dependency-based methodology and theories of social interaction, randomized community trials are advocated as a superior research strategy, one that may help social epidemiology answer the causal questions necessary for remediating disparities and otherwise improving the public's health.

Bias↗

PubMed2000

Accident reduction factors and causal inference in traffic safety studies: a review.

Accident reduction factors are used to predict the change in accident occurrence which a countermeasure can be expected to cause. Since ethical and legal obstacles preclude the use of randomized experiments when evaluating traffic safety improvements, empirical support for the causal effectiveness of accident countermeasures comes entirely from observational studies. Drawing on developments in causal inference initiated by Donald Rubin, it is argued here that the mechanism by which sites are selected for application of a countermeasure should be included as part of a study's data model, and that when important features of the selection mechanism are neglected, existing methods for estimating accident reduction factors become inconsistent. A promising, but neglected, way out of these difficulties lies in developing rational countermeasure selection methods which also support valid causal inference of countermeasure effects.

Accidents, Traffic↗

PubMed1998

Conditional reasoning, causality, and the structure of semantic memory: strength of association as a predictive factor for content effects.

Available evidence indicates that responses to conditional inferences using concrete causal premises is affected by the relative number of available alternate causes (Cummins, D.D., 1995. Memory and Cognition 23 (5), 646-658). We propose that another important factor that may influence the kinds of inferences made to causal conditionals is the relative strength of association between such causes and the consequent term. We present a study with adult participants that examines the effect of strength of association on performance on a conditional reasoning task using causal premises for which there exist one highly associated potential cause for the given consequent term. We predicted that adults would produce a greater proportion of biconditional responses to invalid forms with strongly associated premises than weakly associated ones, while valid forms would not be affected by strength of association. The results are consistent with this hypothesis.

Adult↗

PubMed2001

Childhood victimization and alcohol symptoms in females: causal inferences and hypothesized mediators.

OBJECTIVE: Previous research has reported a relationship between childhood victimization experiences and alcohol problems in females. This paper has two distinct goals: (1) to determine whether it is appropriate to make a causal inference regarding the association between early child abuse and neglect and alcohol symptoms in females; and (2) to examine five potential mechanisms (depression, worthlessness, social isolation/loneliness, low self esteem, and using alcohol and/or drugs to cope) that may mediate the relationship between child abuse and neglect and alcohol symptomatology. METHOD: Substantiated cases of child abuse and neglect from 1967 to 1971 were matched on sex, age, race, and approximate social class with nonabused and non-neglected children and followed prospectively into young adulthood. Subjects were administered a 2-h in-person interview, including the NIMH Diagnostic Interview Schedule (DIS-III-R) to assess alcohol use and abuse. Analyses are restricted to females in the sample (N = 582). RESULTS: Structural equation modeling provides support for the inference that childhood victimization plays a causal role in the development of alcohol symptoms in women. There also is support for the hypothesized mediating role of two mechanisms (depression and using alcohol/drugs to cope), but not for the other mediators. CONCLUSIONS: Evidence from this prospective study suggests that childhood victimization may be one of the causal factors in the development of alcohol problems in females. Interventions should bedirected at abused and neglected females of all ages to help them to deal with depression and to develop coping strategies to prevent the development of serious alcohol problems.

Adaptation, Psychological↗

PubMed1998

Causal webs, preventive brooms, and housekeepers.

The concept of causal webs, which emphasises complex multiple causation of disease and injury, diverts attention and resources from preventive brooms that can sweep away the webs to reduce harm despite multiple causation. Epidemiologists who focus primarily on specification of causal paths in webs may overlook the modifiable necessary conditions for disease and injury. Necessary causes for disease and injury and necessary conditions for prevention are not always the same. Corporations and governments have the opportunity and means to reduce or eliminate many hazards to human health by changing products and processes that are hazardous. The arguments used to avoid doing so are often flawed and sometimes false. Public health scientists who focus on the behavior of corporations and government agencies that fail to employ preventive brooms of known effectiveness are more likely to contribute to harm reduction than those who focus on causal webs.

Accidents, Traffic↗

PubMed2002

Toward a better understanding of the influences on physical activity: the role of determinants, correlates, causal variables, mediators, moderators, and confounders.

BACKGROUND: For research on physical activity interventions to progress systematically, the mechanisms of action must be studied. In doing so, the research methods and their associated concepts and terminology become more complex. It is particularly important to clearly distinguish among determinants, correlates, mediators, moderators, and confounder variables used in physical activity research. This article examines the factors that are correlated with and that may have a causal relationship to physical activity. METHODS AND RESULTS: We propose that the term "correlate" be used, instead of "determinant," to describe statistical associations or correlations between measured variables and physical activity. Studies of the correlates of physical activity are reviewed. The findings of these studies can help to critique existing theories of health behavior change and can provide hypotheses to be tested in intervention studies from which it is possible to draw causal inferences. Mediator, moderator, and confounder variables can act to influence measured changes in physical activity. Intervening causal variables that are necessary to complete a cause-effect pathway between an intervention and physical activity are termed "mediators." The relationship between an intervention and physical activity behaviors may vary for different groups; the strata by which they vary are levels of "moderators" of the relationship. Other factors may distort or affect the observed relationships between program exposure and physical activity, and are known as "confounders." CONCLUSIONS: Consistent use of terms and additional research on mediators and moderators of intervention effects will improve our ability to understand and influence physical activity.

Behavioral Sciences↗

PubMed2004

Detection of causal relationships between factors influencing adverse side-effects from anaesthesia and convalescence following surgery: a path analytical approach.

BACKGROUND AND OBJECTIVE: The anaesthesiologist's preoperative interview with the patient is important in preparing the patient for surgery. Its potential protective influence on adverse side-effects from anaesthesia and convalescence is rarely investigated within the context of other perioperative factors. Structural equation modelling allows detection and quantification of all causal relationships and mediator effects in multivariate models. Therefore, this method is presented as a tool and applied to discover the influence of the preoperative interview within socio-demographic variables and duration of surgery on complaints and recovery after anaesthesia. METHODS: The influence of individual satisfaction with the anaesthesiologist's preoperative interview on postoperative events such as nausea/vomiting, difficulties in recovering from anaesthesia, experience of postoperative pain, physical discomfort and satisfaction with convalescence expressed by the patient was analysed by means of structural equation modelling. The variables gender, age and duration of surgery were also included as predictors in the analyses. The model in the total sample of 710 patients was then analysed for structural differences between groups treated either with propofol (n = 204) or with isoflurane + nitrous oxide (n = 267) for maintenance of anaesthesia. RESULTS: The model revealed that the anaesthesiologist's preoperative interview in combination with associated mediating side-effects explains 45% of the variance of 'feeling physical discomfort' and 18% of the variance of 'satisfaction with convalescence'. The same model could be fitted in the propofol and the isoflurane + nitrous oxide group. Moreover, the structure and the strength of causal relations between variables were identical in the two groups. CONCLUSIONS: The anaesthesiologist's efforts to improve the interview with the patient by more reassuring and proper information will result in less side-effects from anaesthesia and better recovery from surgery. It could be demonstrated that structural equation modelling is a powerful tool for detection of causal relationships and mediator effects in perioperative medicine.

Adolescent↗

PubMed1993

The use of causal indicators in covariance structure models: some practical issues.

In conventional representations of covariance structure models, indicators are defined as linear functions of latent variables, plus error. In an alternative representation, constructs can be defined as linear functions of their indicators, called causal indicators, plus an error term. Such constructs are not latent variables but composite variables, and they have no indicators in the conventional sense. The presence of composite variables in a model can, in some situations, result in problems with identification of model parameters. Also, the use of causal indicators can produce models that imply zero correlation among many measured variables, a problem resolved only by the inclusion of a potentially large number of additional parameters. These phenomena are demonstrated with an example, and general principles underlying them are discussed. Remedies are described so as to allow for the evaluation of models that contain causal indicators.

Analysis of Variance↗

PubMed2005

The experimental task influences cue competition in human causal learning.

Participants were shown A+ and C- trials followed by AB+ and CD+ trials. These trials were embedded in a causal learning task in which participants had to learn either the relationship between different foods and allergic reactions or the relationship between different stocks and an increase in the stock market index. The authors orthogonally varied the manner in which the different cues were presented to participants during training. Cue competition was related to the causal learning scenario but not to the manner in which the different cues were presented. These results question claims of a human bias toward configural processing that were based on difficulties in finding cue competition in some previous causal learning experiments.

Adult↗

PubMed2000

A tetrad test for causal indicators.

The authors propose a confirmatory tetrad analysis test to distinguish causal from effect indicators in structural equation models. The test uses "nested" vanishing tetrads that are often implied when comparing causal and effect indicator models. The authors present typical models that researchers can use to determine the vanishing tetrads for 4 or more variables. They also provide the vanishing tetrads for mixtures of causal and effect indicators, for models with fewer than 4 indicators per latent variable, or for cases with correlated errors. The authors illustrate the test results for several simulation and empirical examples and emphasize that their technique is a theory-testing rather than a model-generating approach. They also review limitations of the procedure including the indistinguishable tetrad equivalent models, the largely unknown finite sample behavior of the test statistic, and the inability of any procedure to fully validate a model specification.

Causality↗

PubMed1992

Causal coherence in the oral narratives of Spanish-speaking children.

Forty-six Spanish-speaking children distributed among three age groups (4:0-4:11, 6:0-6:11, and 8:0-8:11 years) were shown a short silent film and asked to tell the investigator what happened in the movie. All narratives were audiotaped and transcribed for analysis. The stories of older children contained more narrative actions and included more mental state/goal causes than those of younger children. With increasing age, children's narratives showed a decrease in the use of two-clause causal sequences, an increase in the use of three-clause causal sequences, and a decrease in the proportion of unrelated statements. The variability in the types of causal links manifested in the stories suggests that absence of certain types of interclausal connections should not be interpreted as reflecting cognitive or comprehension deficits.

Causality↗

PubMed1998

Bias versus causality: interpreting recent evidence of oral contraceptive studies.

Late in 1995 and early 1996, 4 epidemiologic studies were published that resulted in a crude mean weighted relative risk of approximately 2 when third-generation oral contraceptives were compared with second-generation oral contraceptives as risk factors for venous thromboembolism. This article reviews empirical evidence on bias or systematic error that may have influenced the estimates of association. The Bradford-Hill criteria to distinguish causality from an observed association were used to consider whether third-generation oral contraceptives cause an apparent excess in the occurrence of venous thromboembolism. Bias is more likely than a causal relationship to explain the associations observed for venous thromboembolism. For myocardial infarction, bias may mask the full benefit of third-generation oral contraceptives. For stroke, the question of causality is moot because statistically significant differences between third- and second-generation products have not been detected. The clinical importance and the public health significance of any differences among the various products with respect to adverse cardiovascular outcomes are trivial and undetectable because of the extremely low incidence of those disorders among users of oral contraceptives. The oral contraceptive pill is 99.9% effective when used correctly. All oral contraceptives on the market are safe and getting safer.

Bias↗

PubMed2003

Causal inference in lead research: introduction to the special section on the neurobehavioral effects of environmental lead.

This Special Section on the Neurobehavioral Effects of Environmental Lead includes empirical reports from three major longitudinal prospective studies and a review of animal research modeling effects of lead on human cognition. This Introductory article addresses a fundamental controversy in lead and other epidemiologic research. It is argued that, contrary to conventional wisdom, valid causal inferences can be made from correlational research. Justification for this is to be found in contemporary frameworks drawn from the field of the philosophy of science; an essential, but often neglected perspective for such metatheoretical issues as causal inference. It is concluded that the nonexperimental methods described in the articles comprising this Special Section can, indeed, contribute to causal formulations of lead effects on human development, and that such inferences are not exclusive to experimental evidence.

Causality↗

PubMed2001

The development and initial validation of the Exercise Causality Orientations Scale.

Deci and Ryan's causality orientations theory suggests that there are individual differences in motivational orientation towards initiating and regulating behaviour. They described three causality orientations: autonomy, control and impersonal. The aim of this paper is to describe the development and concurrent validity of the Exercise Causality Orientations Scale (ECOS), which was designed to measure the strength of these three orientations within exercise. Altogether, 592 working adults aged 35.0 +/- 11.4 years (mean +/- s) completed the ECOS and measures of self-determination, self-consciousness and social desirability. The analysis was conducted in two parts. First, the data were subjected to confirmatory factor analysis using a multi-trait, multi-method framework. The original model resulted in a poor fit to the data. On the basis of its modification indices, three scenarios with ambiguous items were removed successively, resulting in a scale with good psychometric properties. Secondly, Pearson's correlations were conducted between the subscales of the ECOS and those of the questionnaires used for validation. Most of the results supported a priori hypotheses. In conclusion, our results show the ECOS to have good psychometric properties and they provide some support for its concurrent validity.

Adult↗

PubMed2005

Cue interaction effects in causal judgement: an interpretation in terms of the evidential evaluation model.

In judging the extent to which a cue causes an outcome, judgement can be affected by information about other cues that are correlated with the one being judged. These cue interaction effects have usually been interpreted in terms of associative learning processes. I propose that a different model of causal judgement, the evidential evaluation model, offers a viable alternative interpretation of cue interaction phenomena. Under the evidential evaluation model, instances of contingency information are interpreted as evidence, which is confirmatory, disconfirmatory, or irrelevant for the cue being judged. When two cues co-occur in a set of instances the evidential value of the instances for one of them is determined by three factors: the proportion of confirming instances in the set; disambiguation value, which concerns the relation between the set of information and prior beliefs about the co-occurring cue; and confirmation value, which concerns the relation between the set of information and prior beliefs about the cue being judged. Any previous judgement of the cue is then modified in the light of these. It is shown that this model can account for all the cue interaction phenomena that have been investigated in studies of human causal judgement. The model also generates novel predictions, and the results of three experiments give support to these predictions. It is also shown that several other current models of causal judgement fail to predict a key result from Experiment 3.

Analysis of Variance↗