ReviewFrontiers in genetics2019
Review of Causal Discovery Methods Based on Graphical Models.
Review in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 161 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
161 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Causal inference in the diagnosis and prognosis of ovarian cancer: current state and future directions.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Pooled it
- Methodological Considerations for Studies in Sport and Exercise Science with Women as Participants Part II: Guidance for Applied Studies in Elite Female Athletes.Sports medicine (Auckland, N.Z.) · 2026Article
- Towards precision medicine: Application of the structural causal model in psychiatry.General psychiatry · 2026Article
- Article
- Toward A Pre-disease State-centered New Paradigm in Multi-omics Research.Genomics, proteomics & bioinformatics · 2026Article
- Structural equation modeling to explore putative causal factors for chronic fatigue in childhood cancer survivors: a DCCSS LATER study.Journal of cancer survivorship : research and practice · 2026Article
- Foundation models in biomedical imaging: turning hype into reality.Nature biomedical engineering · 2026Review
- Ten quick tips for causal analysis of biomedical omics data.PLoS computational biology · 2026Article
- Psychological stress induced mitochondrial response in humans and its medial prefrontal biomarker correlates.Translational psychiatry · 2026Article
- Genetic analysis of imaging-derived phenotypes.Nature reviews. Genetics · 2026Review
- Environmental drivers of low vaccine responsiveness in a lab-to-wild rodent model.PLoS pathogens · 2026Article
- Historical Perspectives, Classification and Diagnostic Approaches of Inborn Errors of Metabolism: A Systematic Review and Meta-Analysis.Metabolites · 2026Review
- Toward Causal Relationships of Intracranial Pressure, Cerebrovascular Reactivity, Interventions, and Outcome in Severe Traumatic Brain Injury: A CENTER-TBI High-Resolution Substudy.Neurocritical care · 2026Article
- A causal discovery framework for analyzing the impact mechanism of generative AI on digital literacy: an LLM-based behavioral analysis approach.Scientific reports · 2026Article
- Causal Structure Learning Assumptions Shape Counterfactual Safety: Expert-Guided Constraints vs. Data-Driven DAGs with Probabilistic Logic Twin Networks.Entropy (Basel, Switzerland) · 2026Article
- Electronic medical record-based causal network modeling for acute myocardial infarction diagnosis in the emergency department.iScience · 2026Article
- Bayesian Networks and Causal Discovery.Entropy (Basel, Switzerland) · 2026Article
- Causal Graphical Models and Their Applications.Entropy (Basel, Switzerland) · 2026Article
- SELF-Tree: An Interpretable Model for Multivariate Causal Direction Heterogeneity Analysis.Psychometrika · 2026Article
- A Deep Spatio-Temporal Architecture for Dynamic ECN Analysis with Granger Causality based Causal Discovery.Pattern recognition · 2026Article
101 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
A fundamental task in various disciplines of science, including biology, is to find underlying causal relations and make use of them. Causal relations can be seen if interventions are properly applied; however, in many cases they are difficult or even impossible to conduct. It is then necessary to discover causal relations by analyzing statistical properties of purely observational data, which is known as causal discovery or causal structure search. This paper aims to give a introduction to and a brief review of the computational methods for causal discovery that were developed in the past three decades, including constraint-based and score-based methods and those based on functional causal models, supplemented by some illustrations and applications.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.