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Results for “causal inference”

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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↗

PubMed1998

Agents, vehicles, and causal inference in bacterial foodborne disease outbreaks: 82 reports (1986-1995).

OBJECTIVE: To examine the study design of, and the practice of causal inference in, investigations of bacterial foodborne disease outbreaks occurring in the United States and to summarize agents and vehicles identified. DESIGN: Retrospective study. PROCEDURE: An online medical reference database was searched for reports of bacterial foodborne disease outbreak investigations published between 1986 and 1995. Reports were retrieved and reviewed for use of 9 causal criteria in investigations. Information on etiologic agents, vehicles, seasonality, and primary study design from each outbreak was also retrieved. RESULTS: 82 reports were retrieved and reviewed. Coherence, consistency, temporality, and strength of association were the causal criteria most commonly used in foodborne disease outbreak investigations. Coherence was used in all investigations. The number of criteria used ranged from 3 to 7. Meat (n = 20) and eggs (12) were the most commonly implicated vehicles. Salmonella sp and Escherichia coli O157:H7 accounted for 55% of agents reportedly isolated. Cohort and case-control methods were the most common study designs. CLINICAL RELEVANCE: Patterns were found in the use of causal criteria in foodborne disease outbreak investigations. These criteria can provide veterinarians and other public health practitioners with a means to effectively conceptualize, communicate, and summarize causal conclusions. The 4 most commonly used criteria may represent core criteria that investigators consider most useful in explaining food-borne disease outbreaks.

Animals↗

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↗

PubMed2000

Lines that connect: assessing the causality inference in the case of particulate pollution.

The question of when it would be appropriate to conclude that the associations between particulate pollution and various outcomes (including mortality) should be judged as causal in nature has been difficult and controversial. Although such a judgment must be subject to revision, the volume of new information and new experimental findings has been so great that such a reevaluation is required at frequent intervals. The useful summary by Gamble [PM(2. 5) and Mortality in Long-Term Prospective Cohort Studies: Cause-Effect or Statistical Associations? Environ Health Perspect 106:535-554 (1998)] of the reasons why a causal inference was, in his opinion, not justified provides a basis for reevaluation in the light of new data. Such a reexamination indicates that the associative evidence is now stronger and that the biologic basis for a number of adverse effects has now been demonstrated. All of the useful guideline criteria customarily applied to such questions seem to have been met, although there is still much to be learned about interactive effects and the possibility of statistical thresholds.

Air Pollution↗

PubMed2026

Endocrine-disrupting chemical-induced gene networks confer coronary heart disease risk revealed by causal inference and single-cell analyses.

BACKGROUND: Endocrine-disrupting chemicals (EDCs) are linked to coronary heart disease (CHD), but underlying mechanisms remain unclear. We aimed to identify EDC-related genes and evaluate their causal roles in CHD. METHODS: We curated EDC-related genes from a compound-gene interaction database and integrated them with CHD genome-wide association study (GWAS) summary statistics and tissue-specific expression quantitative trait loci (eQTL) data. Two-sample Mendelian randomization (MR) and Bayesian colocalization were applied to infer causality. Functional enrichment, single-cell RNA sequencing of human coronary arteries, and EDC-gene networks were further analyzed. RESULTS: After FDR correction, 39 genes were significantly associated with CHD risk via MR. Four genes-ZNF827, FCHO1, IPO9 (protective), and RPL13 (risk-increasing)-showed strong colocalization (PPH4 > 0.9). Pathway and single-cell analyses of coronary artery tissue indicated that vascular and immune pathways mediate these effects. An interaction network highlighted associations between specific EDCs and candidate genes implicated in CHD susceptibility. CONCLUSION: This integrative genomic study provides evidence that EDCs influence CHD susceptibility through distinct gene networks, revealing potential mechanisms and molecular targets for prevention and therapy.

Humans↗

PubMed2001

[Causal inference in medicine--decision making].

In the field of occupational medicine, either when we consider some preventive plans or when we make decisions to compensate for occupational diseases, it has been necessary to discuss causality between work and disease. Furthermore, epidemiologic causality has recently been used in risk assessment in occupational and environmental settings. We have shown that the law of causality in medicine is recognized as probability and continuous variables. Such a law of causality has been recognized in the same way as probability in physics, too, and has been regarded as a model of science. Physicists and mathematicians had claimed the importance of probability in causal inference as well as the principle of uncertainty before it was discovered. We, then, explained Etiologic Fraction (EF), Attributable Proportion for the Exposed Population (APE), Probability of Causation (PC), and so on. The PC has been used to ascertain the conditional probability in an individual case of a disease having been caused by a particular prior exposure, by using the experience of exposed populations to determine the appropriate relative risk, and this has been used for compensation for exposed cases. Next the applicability of information from a population to individuals was presented. Third, we provided a brief historical aspect of epidemiology. The evolutions in Epidemiology have been very rapid, so we pointed out that, in Japan, we could observe many incommensurable phenomena in epidemiologists and physicians depending on the era which was studied by them. Fourth, we discussed judgement and political application based on epidemiologic evidence, using Yanagimoto's classification is also taken or not should be estimated and compared. We presented several examples of reasoning in judgements. Lastly, we discussed several tasks and assignments for the future of epidemiology.

Causality↗

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↗

PubMed2006

Reasoning rats: forward blocking in Pavlovian animal conditioning is sensitive to constraints of causal inference.

Forward blocking is one of the best-documented phenomena in Pavlovian animal conditioning. According to contemporary associative learning theories, forward blocking arises directly from the hardwired basic learning rules that govern the acquisition or expression of associations. Contrary to this view, here the authors demonstrate that blocking in rats is flexible and sensitive to constraints of causal inference, such as violation of additivity and ceiling considerations. This suggests that complex cognitive processes akin to causal inferential reasoning are involved in a well-established Pavlovian animal conditioning phenomenon commonly attributed to the operation of basic associative processes.

Animals↗

PubMed2005

Inferring causality in brain images: a perturbation approach.

When engaged by a stimulus, different nodes of a neural circuit respond in a coordinated fashion. We often ask whether there is a cause and effect in such interregional interactions. This paper proposes that we can infer causality in functional connectivity by employing a 'perturb and measure' approach. In the human brain, this has been achieved by combining transcranial magnetic stimulation (TMS) with positron emission tomography (PET), functional magnetic resonance imaging or electroencephalography. Here, I will illustrate this approach by reviewing some of our TMS/PET work, and will conclude by discussing a few methodological and theoretical challenges facing those studying neural connectivity using a perturbation.

Brain Mapping↗

PubMed1999

Inferring causality from relational data and designs: historical and contemporary lessons for research and clinical practice.

My address to you today is born of frustration with the growing claims, evident in the research literature and in clinical reports that happen to cross my desk, of causation made on the basis of correlational data. I recall, as a sophomore in college, being taught early in my first experimental psychology course, "you cannot infer causality from correlation." This point was made adamant in my first face-to-face critique of an experimental psychology research paper by Dr. Robert T. Brown, who pointed out, in lowering my grade, that I had inferred organic causes to behavior patterns in gerbils based solely on correlational data. This caution was reiterated in my statistics courses until it must have been indelibly stamped upon my then still somewhat plastic brain.

Analysis of Variance↗

PubMed2000

Epidemiologic evidence and causal inference.

Preventing cancer depends on the ability to recognize and remove causal factors. In current practice, the methods used to judge cause from epidemiologic, clinical trials and biologic evidence include systematic narrative reviews, criteria-based inference methods, and meta-analysis. Subjectivity and values play a key role in the practice of causal inference, especially in selecting criteria and assigning rules of evidence to those criteria. Judging cause is a central concern of physicians, epidemiologists, and other public health professionals committed to cancer prevention.

Causality↗

PubMed2006

Limits to causal inference based on Mendelian randomization: a comparison with randomized controlled trials.

"Mendelian randomization" refers to the random assortment of genes transferred from parent to offspring at the time of gamete formation. This process has been compared to a randomized controlled trial of genetic variants. This could greatly aid observational epidemiology by potentially allowing an unbiased estimate of the effects of gene products on disease outcomes. However, studies utilizing Mendelian randomization to estimate effects of gene products on outcomes should be interpreted with caution. In this paper, the authors discuss some of the challenges facing epidemiologists in the analysis and interpretation of Mendelian randomization studies, particularly those that become apparent when the analogy with randomized controlled trials is closely examined. The authors conclude that Mendelian randomization is a powerful addition to etiologic research tools. However, care must be taken, because drawing valid causal inferences from its application depends upon more extensive assumptions than are required in randomized controlled trials.

Causality↗

PubMed2025

The Effect of Alcohol Intake on Brain White Matter Microstructural Integrity: A New Causal Inference Framework for Incomplete Phenomic Data.

Although substance use, such as alcohol intake, is known to be associated with cognitive decline during aging, its direct influence on the central nervous system remains incompletely understood. In this study, we investigate the influence of alcohol intake frequency on reduction of brain white matter microstructural integrity in the fornix, a brain region considered a promising marker of age-related microstructural degeneration, using a large UK Biobank (UKB) cohort with extensive phenomic data reflecting a comprehensive lifestyle profile. Two major challenges arise: (a) potentially nonlinear confounding effects from phenomic variables and (b) a limited proportion of participants with complete phenomic data. To address these challenges, we develop a novel ensemble learning framework tailored for robust causal inference and introduce a data integration step to incorporate information from UKB participants with incomplete phenomic data, improving estimation efficiency. Our analysis reveals that daily alcohol intake may significantly reduce fractional anisotropy, a neuroimaging-derived measure of white matter structural integrity, in the fornix and increase systolic and diastolic blood pressure levels. Moreover, extensive numerical studies demonstrate the superiority of our method over competing approaches in terms of estimation bias, while outcome regression-based estimators may be preferred when minimizing mean squared error is prioritized. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Brain aging↗

PubMed2001

Comparing personal trajectories and drawing causal inferences from longitudinal data.

This review considers statistical analysis of data from studies that obtain repeated measures on each of many participants. Such studies aim to describe the average change in populations and to illuminate individual differences in trajectories of change. A person-specific model for the trajectory of each participant is viewed as the foundation of any analysis having these aims. A second, between-person model describes how persons very in their trajectories. This two-stage modeling framework is common to a variety of popular analytic approaches variously labeled hierarchical models, multilevel models, latent growth models, and random coefficient models. Selected published examples reveal how the approach can be flexibly adapted to represent development in domains as diverse as vocabulary growth in early childhood, academic learning, and antisocial propensity during adolescence. The review then considers the problem of drawing causal inferences from repeated measures data.

Adolescent↗

PubMedDate not supplied

God does not play dice: causal determinism and preschoolers' causal inferences.

Three studies investigated children's belief in causal determinism. If children are determinists, they should infer unobserved causes whenever observed causes appear to act stochastically. In Experiment 1, 4-year-olds saw a stochastic generative cause and inferred the existence of an unobserved inhibitory cause. Children traded off inferences about the presence of unobserved inhibitory causes and the absence of unobserved generative causes. In Experiment 2, 4-year-olds used the pattern of indeterminacy to decide whether unobserved variables were generative or inhibitory. Experiment 3 suggested that children (4 years old) resist believing that direct causes can act stochastically, although they accept that events can be stochastically associated. Children's deterministic assumptions seem to support inferences not obtainable from other cues.

Biological Evolution↗

PubMed2005

Young children infer causal strength from probabilities and interventions.

We examine the interaction of two cues that children use to make judgments about cause-effect relations: probabilities and interventions. Children were shown a "detector" that lit up and played music when a block was placed on its surface. We varied the probabilistic effectiveness of the block, as well as whether the experimenter or the child was performing the interventions. In Experiment 1, we found that children can use probabilistic evidence to make inferences about causal strength. However, when the results of their own interventions are in conflict with the overall frequencies, preschoolers favor the results of their own interventions. In Experiment 2, children used probabilistic evidence to infer a hidden causal mechanism. Though they again gave preference to their own interventions, they did not do so when their interventions were explicitly confounded by an alternative cause.

Child↗

PubMed1995

Causal inferences as perceptual judgements.

We analyze how subjects make causal judgements based on contingency information in two paradigms. In the discrete paradigm, subjects are given specific information about the frequency a, with which a purported cause occurs with the effect; the frequency b, with which it occurs without the effect; the frequency c, with which the effect occurs when the cause is absent; and the frequency d, with which both cause and effect are absent. Subjects respond to P1 = a/(a + b) and P2 = c/(c + d). Some subjects' ratings are just a function of P1 while others are a function of delta P = P1-P2. Subjects' post-experiment reports are accurate reflections of which model they use. Combining these two types of subjects results in data well fit by the weighted delta P model (Allan, 1993). In the continuous paradigm, subjects control the purported causes (by clicking a mouse) and observe whether an effect occurs. Because cause and effects occur continuously in time, it is not possible to explicitly pair causes and effects. Rather, subjects report that they are responding to the rate at which the effects occur when they click versus when they do not click. Their ratings are a function of rates and not probabilities. In general, we argue that subjects' causal ratings are judgments of the magnitude of perceptually salient variables in the experiment.

Adult↗

PubMed1997

[Alcohol, breast cancer and causal inference in epidemiology].

In order to demonstrate how possible causal relationships are critically evaluated in epidemiologic research, literature on the association between alcohol and breast cancer is reviewed and discussed. A cause can be defined as a factor which, in combination with other factors, known and unknown, is sufficient to produce an effect. Since the hypothesis-generating study was published in 1977, a total of 34 positive and 16 negative studies have been published. Methodological problems, such as chance, bias and confounding, cannot be considered as plausible explanations for the above majority of positive findings. The question of causality was then evaluated using the guidelines developed by Bradford Hill in 1965. Among these, the strength of the association, consistency, temporality, biological gradient and biological plausibility, are the most important. In spite of the relatively weak association and somewhat inconsistent results, it is concluded that alcohol consumption should be considered as a cause of breast cancer. It is estimated that in Norway, between 24 and 180 cases of breast cancer may be attributed to alcohol consumption. Future research should focus on the question of effect-modification and on the possible implications of different patterns of alcohol consumption.

Alcohol Drinking↗