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PubMed2005

Bounding causal effects under uncontrolled confounding using counterfactuals.

Common sensitivity analysis methods for unmeasured confounders provide a corrected point estimate of causal effect for each specified set of unknown parameter values. This article reviews alternative methods for generating deterministic nonparametric bounds on the magnitude of the causal effect using linear programming methods and potential outcomes models. The bounds are generated using only the observed table. We then demonstrate how these bound widths may be reduced through assumptions regarding the potential outcomes under various exposure regimens. We illustrate this linear programming approach using data from the Cooperative Cardiovascular Project. These bounds on causal effect under uncontrolled confounding complement standard sensitivity analyses by providing a range within which the causal effect must lie given the validity of the assumptions.

Adrenergic beta-Antagonists↗

PubMed2025

Causal relationship between frailty and diabetes subtypes: A bidirectional Mendelian randomization study.

Frailty and diabetes mellitus (DM) are closely linked, but their causal relationship remains unclear. This study aims to determine the bidirectional causal relationship between frailty and different DM subtypes using Mendelian randomization (MR). We performed a 2-sample MR analysis using summary statistics from large-scale genome-wide association studies. The inverse-variance weighting method was the primary analytical approach, with MR-Egger regression and weighted median methods for sensitivity analysis. Horizontal pleiotropy and heterogeneity were assessed using MR-PRESSO and Cochran Q test. Genetically predicted frailty was significantly associated with an increased risk of type 2 diabetes (T2DM) and gestational diabetes (GDM) (odds ratio [OR] = 2.142, 95% confidence interval [CI]: 1.751-2.621, P < .001; OR = 2.280, 95% CI: 1.368-3.800, P = .002), but no causal relationship was observed for type 1 diabetes or glycemic traits (P > .05). Conversely, genetically predicted type 1 diabetes, T2DM, GDM, and postprandial glucose levels (2-hour post-load glucose) increased the risk of frailty (OR = 1.026, 95% CI: 1.014-1.038, P < .001; OR = 1.046, 95% CI: 1.033-1.058, P < .001; OR = 1.068, 95% CI: 1.040-1.096, P < .001; OR = 1.095, 95% CI: 1.049-1.144, P < .001). Sensitivity analyses confirmed the robustness of these findings. This study provides genetic evidence supporting a bidirectional causal relationship between frailty and diabetes, particularly T2DM and GDM. These findings highlight the need for early frailty screening in diabetic patients and better metabolic management in frail populations.

Humans↗

PubMed2005

Veterinary pharmacovigilance. Part 5. Causality and expectedness.

In the European Union and elsewhere there is a requirement to ascribe causality to adverse drug reactions which occur in treated animals. In the EU, the ABON system of causality assessment is used but the assignment of causality assigned is not always self evident, and it may be complicated for a variety of reasons. In this paper, the approaches to causality assessment, based on a number of key criteria which examine the administration of the drug in relation to the sequence of ensuing events and the presence of biological plausibility are examined, along with the utility of using algorithms to facilitate this process. Unexpected adverse drug reactions usually require expedited reporting, depending on national or regional regulatory requirements. Again, deciding on what might constitute an expected (or unexpected) adverse reaction, particularly when a product may be intended for use in a number of species, and when within any one species a number of breeds may be treated, is not necessarily a straightforward issue. However, an approach to facilitate the decision- making process, based on a similar approach used in the pharmacovigilance of human medicinal products is discussed.

Adverse Drug Reaction Reporting Systems↗

PubMed2006

Polydesigns and causal inference.

In an increasingly common class of studies, the goal is to evaluate causal effects of treatments that are only partially controlled by the investigator. In such studies there are two conflicting features: (1) a model on the full cohort design and data can identify the causal effects of interest, but can be sensitive to extreme regions of that design's data, where model specification can have more impact; and (2) models on a reduced design (i.e., a subset of the full data), for example, conditional likelihood on matched subsets of data, can avoid such sensitivity, but do not generally identify the causal effects. We propose a framework to assess how inference is sensitive to designs by exploring combinations of both the full and reduced designs. We show that using such a "polydesign" framework generates a rich class of methods that can identify causal effects and that can also be more robust to model specification than methods using only the full design. We discuss implementation of polydesign methods, and provide an illustration in the evaluation of a needle exchange program.

Biometry↗

PubMed1996

Causal beliefs and acute psychiatric hospital admission.

Causal beliefs regarding the onset of psychiatric disorder and treatment expectations for clients admitted to a psychiatric unit were examined from the points of view of the clients themselves, their relatives and staff. Results suggested that these beliefs were influenced by the admission itself. Those clients who had been admitted previously held significantly higher biological causal beliefs than those who had been admitted for the first time; as compared to staff who did not distinguish between the two groups in this way. The mean psychosocial views of relatives decreased significantly over the two-month period following an admission. Clients' and relatives' mean biological views were significantly greater than those of staff. Various correlations were found between causal beliefs and treatment expectations including one in the relative's' view between psychosocial causal beliefs and the importance assigned to family therapy. Indeed, there was a significant decrease in the family therapy rating over time given by relatives.

Adolescent↗

PubMed2001

Enhancing causal interpretations of quality improvement interventions.

In an era of chronic resource scarcity it is critical that quality improvement professionals have confidence that their project activities cause measured change. A commonly used research design, the single group pre-test/post-test design, provides little insight into whether quality improvement interventions cause measured outcomes. A re-evaluation of a quality improvement programme designed to reduce the percentage of bilateral cardiac catheterisations for the period from January 1991 to October 1996 in three catheterisation laboratories in a north eastern state in the USA was performed using an interrupted time series design with switching replications. The accuracy and causal interpretability of the findings were considerably improved compared with the original evaluation design. Moreover, the re-evaluation provided tangible evidence in support of the suggestion that more rigorous designs can and should be more widely employed to improve the causal interpretability of quality improvement efforts. Evaluation designs for quality improvement projects should be constructed to provide a reasonable opportunity, given available time and resources, for causal interpretation of the results. Evaluators of quality improvement initiatives may infrequently have access to randomised designs. Nonetheless, as shown here, other very rigorous research designs are available for improving causal interpretability. Unilateral methodological surrender need not be the only alternative to randomised experiments.

Cardiac Catheterization↗

PubMed2003

Vascular disorders preceding diagnosis of cancer: distinguishing the causal relationship based on Bradford-Hill guidelines.

The literature investigating the association between vascular disorders and malignant neoplasms does not comprehensively review the full spectrum of vascular disorders associated with cancer, or provide proof that cancer is an etiologic factor in the development of these disorders. This paper investigates the causal role of cancer in the pathogenesis of vascular disorders, based on the Bradford-Hill criteria of causation. The Medline database was searched for articles on vascular disorders preceding the diagnosis of cancer (VDPCD). Included in the analysis were vascular disorders caused either by direct tumoral involvement of vessels or by paraneoplastic mechanisms. Vascular disorders caused by adverse reactions to anticancer therapy were excluded from analysis. Seven categories of VDPCDs were recognized: venous thromboembolism, arterial thrombosis and embolism, nonbacterial thrombotic endocarditis, migratory superficial thrombophlebitis, vasculitis, thrombotic microangiopathy, and leukothrombosis. To establish causality of the association between VDPCDs and malignancy, the degree of fulfillment of the Bradford-Hill criteria was assessed. A strong association was found in the literature between venous thromboembolism and cancer (OR 2.3-14.9 and SIR 1.3-4.4). Consistency and temporality of the association were confirmed in all VDPCD variants. Seven Bradford-Hill criteria were fulfilled for cancer associated with venous thromboembolism, six criteria for superficial phlebitis and cancer, and five criteria for each of the other VDPCDs. In conclusion, these data support the causal role of cancer in the pathogenesis of all seven categories of VDPCDs. Recognition of such a causal link between cancer and various vascular disorders may promote an earlier cancer diagnosis.

Causality↗

PubMed1996

[Causal inference in medicine--a historical view in epidemiology].

Changes of causal inference concepts in medicine, especially those having to do with chronic diseases, were reviewed. The review is divided into five sections. First, several articles on the increased academic acceptance of observational research are cited. Second, the definitions of confounder and effect modifier concepts are explained. Third, the debate over the so-called "criteria for causal inference" was discussed. Many articles have pointed out various problems related to the lack of logical bases for standard criteria, however, such criteria continue to be misapplied in Japan. Fourth, the Popperian and verificationist concepts of causal inference are summarized. Lastly, a recent controversy on meta-analysis is explained. Causal inference plays an important role in epidemiologic theory and medicine. However, because this concept has not been well-introduced in Japan, there has been much misuse of the concept, especially when used for conventional criteria.

Causality↗

PubMedDate not supplied

Causality and control: key to the experiment.

The main aim of the experimental approach in research is to discover causal relationships between variables. This article describes the concept of causality and discusses common and important aspects and factors related to causality, manipulation and control. The four principles which underpin the inference of causality in experiments are highlighted and the role of 'control' in achieving the aim of experimental studies is discussed. Some methods of control such as manipulation, randomization, matching subjects, and holding extraneous variables constant are considered. Experimental designs are underpinned by these concepts. Indication is given of the threats to internal validity, which will be discussed in detail in the next article in this series.

Causality↗

PubMed2000

Causal attribution and Mill's methods of experimental inquiry: past, present and prospect.

J. S. Mill proposed a set of Methods of Experimental Inquiry that were intended to guide causal inference under every conceivable set of circumstances in which experiments or observations could be carried out. The conceptual and historical relationship between these Methods and modern models of causal attribution is investigated. Mill's work retains contemporary relevance because his insights show how research can progress into presently uncharted waters. Following Mill, it is proposed that people use many different methods of causal attribution, the nature of which remains to be ascertained, and that the conditions that affect choice of method include the need to eliminate alternative causal candidates, whether single or multiple events are to be explained, the use of intervention or experiment as opposed to mere observation, and practical concerns.

Causality↗

PubMed2005

Causal beliefs and behaviour change post-myocardial infarction: how are they related?

INTRODUCTION: Weinman, Petrie, Sharpe, and Walker (2000) showed that the causal attributions of a sample of first-time myocardial infarction (MI) patients and their spouses from Auckland, New Zealand, were associated with changes in health-related behaviour over the first 6 months post-MI. However, their analyses did not control for pre-MI health-related behaviour. METHOD: This paper reports a re-analyses of the Auckland data, and a replication study conducted with 155 first-time MI patients in Brighton, United Kingdom (UK), to investigate whether baseline attributions for MI were related to health-related behaviour change at 6 months (N=132). Spouses (N=85) also completed the attribution questionnaire at baseline. RESULTS: There was no consistent relationship between the causal attributions of patients and subsequent behaviour change in Auckland and Brighton. For both samples, causal attributions were associated with pre-MI behaviour. CONCLUSIONS: The data from both samples suggest that the causal attributions of MI patients and their spouses may be realistic, but not predictive of subsequent changes in behaviour.

Aged↗

PubMed2005

Remarks on the analysis of causal relationships in population research.

The problem of determining cause and effect is one of the oldest in the social sciences, where laboratory experimentation is generally not possible. This article provides a perspective on the analysis of causal relationships in population research that draws upon recent discussions of this issue in the field of economics. Within economics, thinking about causal estimation has shifted dramatically in the past decade toward a more pessimistic reading of what is possible and a retreat in the ambitiousness of claims of causal determination. In this article, the framework that underlies this conclusion is presented, the central identification problem is discussed in detail, and examples from the field of population research are given. Some of the more important aspects of this framework are related to the problem of the variability of causal effects for different individuals; the relationships among structural forms, reduced forms, and knowledge of mechanisms; the problem of internal versus external validity and the related issue of extrapolation; and the importance of theory and outside evidence.

Causality↗

PubMed2006

Tobacco promotion and the initiation of tobacco use: assessing the evidence for causality.

OBJECTIVE: We sought to determine whether there is evidence of a causal link between exposure to tobacco promotion and the initiation of tobacco use by children. METHODS: We conducted a structured search in Medline, PsycINFO, and ABI/INFORM Global to identify relevant empirical research. The literature was examined against the Hill epidemiologic criteria for determining causality. RESULTS: (1) Children are exposed to tobacco promotion before the initiation of tobacco use; (2) exposure increases the risk for initiation; (3) there is a dose-response relationship, with greater exposure resulting in higher risk; (4) the increased risk is robust; it is observed with various study methods, in multiple populations, and with various forms of promotion and persists after controlling for other factors; (5) scientifically plausible mechanisms whereby promotion could influence initiation exist; and (6) no explanation other than causality can account for the evidence. CONCLUSIONS: Promotions foster positive attitudes, beliefs, and expectations regarding tobacco use. This fosters intentions to use and increases the likelihood of initiation. Greater exposure to promotion leads to higher risk. This is seen in diverse cultures and persists when other risk factors, such as socioeconomic status or parental and peer smoking, are controlled. Causality is the only plausible scientific explanation for the observed data. The evidence satisfies the Hill criteria, indicating that exposure to tobacco promotion causes children to initiate tobacco use.

Adolescent↗

PubMed1995

[Models of causal inference: critical analysis of the use of statistics in epidemiology].

The foundations on which the concept of risk has been constructed are discussed. A description of Rubin's model of causal inference, which was first developed in the domain of applied statistics, and later incorporated into a branch of epidemiology, is taken as the starting point. Analysis of the premisses of causal inference brings to light the logical stages in the construction of the concept of risk, allowing it to be understood "from the inside". The abovementioned branch of statistics and epidemiology seeks to demonstrate that statistics can infer causality instead of simply revealing statistical associations; the model gives the basis for estimating that which way be defined as the effect of a cause. Using this procedural distinction between causal inference and association, the model also seeks to differentiate between the epidemiologial dimension of concepts and the merely statistical dimension. This leads to greater complexity when handing the concepts of interation and coofounding. The redective aspects inherent in this methodological construction of risk are here high lighted. Thus, whether applied to individual or populational inferences, this methodological construction imposes limits that need to be taken into account in its theoretical and practical application to epidemiology.

Causality↗

PubMed2005

Causal influence: advances in neurosignal analysis.

The analysis of multichannel recordings such as electroencephalography (EEG) and magnetoencephalography (MEG) is important both for basic brain research and for medical diagnosis and treatment. Multivariate linear regressive analysis such as the AutoRegressive (MAR) modeling is an effective means to characterize, with high spatial, temporal, and frequency resolution, functional relations within multichannel neuronal data. Recent advances in MAR modeling show promise for the analysis and visualization of large-scale network interactions, especially in the ability to assess their causal relations. This article provides a detailed review of the advances in the development and application of causal influence measures for analyzing neurosignal within the framework of the MAR spectral analysis. First, we outline mathematical formulations of the MAR model and its related estimation procedures, with emphasis on the development of causal influence measures for analyzing brain circuits. Second, we address the technical issues on the practical applications of the causal measures to the neurobiological data. Of particular interest is the recent development of adapting the MAR to analyze neural spike train data. Third, we present a variety of applications ranging from basic neuroscience research to clinical applications as well as functional neuroimaging. We finally conclude with a brief summary and discuss future research development in this field.

Action Potentials↗

PubMed2026

Integrated single-cell RNA sequencing and mendelian randomization analysis identifies causal immune-related driver genes in the heart failure inflammatory microenvironment.

BACKGROUND: Heart failure (HF) is a major global cause of cardiovascular death and disability. Chronic inflammation and immune dysregulation are critical in its development. The cardiac immune microenvironment, especially macrophages, drives HF progression, yet its molecular mechanisms and prognostic impact are not fully clear. This study aimed to identify causal immune-related driver genes in the HF inflammatory microenvironment. METHODS: We combined single-cell RNA sequencing (scRNA-seq) and Mendelian randomization (MR) to study how the inflammatory immune microenvironment affects HF risk. Using two public scRNA-seq datasets, we identified differentially expressed genes (DEGs) in HF heart tissues and selected 489 candidate genes. Causal relationships between these genes and HF were tested using expression quantitative trait loci (eQTL) data and HF genome-wide association study (GWAS) summary statistics. RESULTS: MR analysis showed that 65 genes were causally linked to HF risk. These genes were enriched in pathways related to cardiomyopathy, leukocyte migration, natural killer (NK) cell cytotoxicity, neutrophil extracellular traps, and NF-κB signaling. HF hearts displayed increased levels of macrophages, T cells, B cells, lymphoid cells, and mast cells, while neutrophils were reduced. CONCLUSIONS: Our integrated analysis reveals the central role of the cardiac inflammatory immune microenvironment in HF and identifies 65 key genes causally associated with HF susceptibility. These genes influence specific immune pathways and cell infiltration, shaping HF progression, and provide a basis for developing new biomarkers and immune-targeted therapies.

Heart failure (HF)↗

PubMed2006

When similarity and causality compete in category-based property generalization.

Five experiments were performed to investigate the category-based generalization of nonblank properties, properties that were novel but that were attributed to existing category features with causal explanations. Experiments 1-3 tested how such explanations interact with the well-known effects of similarity on such generalizations. The results showed that when the causal explanations were used, standard effects of typicality (Experiment 1), diversity (Experiment 2), or similarity itself (Experiment 3) were almost completely eliminated. Experiments 4 and 5 demonstrated that category-based generalizations exhibit some of the standard properties of causal reasoning; for example, an effect (i.e., a novel category property) is judged to be more prevalent when its cause (i.e., an existing category feature) is also prevalent. These findings suggest that category-based property generalization is often an instance of causal inference.

Animals↗

PubMed2006

The importance of decision making in causal learning from interventions.

Recent research has focused on how interventions benefit causal learning. This research suggests that the main benefit of interventions is in the temporal and conditional probability information that interventions provide a learner. But when one generates interventions, one must also decide what interventions to generate. In three experiments, we investigated the importance of these decision demands to causal learning. Experiment 1 demonstrated that learners were better at learning causal models when they observed intervention data that they had generated, as opposed to observing data generated by another learner. Experiment 2 demonstrated the same effect between self-generated interventions and interventions learners were forced to make. Experiment 3 demonstrated that when learners observed a sequence of interventions such that the decision-making process that generated those interventions was more readily available, learning was less impaired. These data suggest that decision making may be an important part of causal learning from interventions.

Attention↗