Results 31 to 40 of about 134,347 (253)
Nonlinear causal discovery with confounders. [PDF]
J Am Stat Assoc, 2023 This article introduces a causal discovery method to learn nonlinear relationships in a directed acyclic graph with correlated Gaussian errors due to confounding. First, we derive model identifiability under the sublinear growth assumption. Then, we propose a novel method, named the Deconfounded Functional Structure Estimation (DeFuSE), consisting of a
Li C, Shen X, Pan W.
europepmc +4 more sources
Disentangling causality: assumptions in causal discovery and inference
Artificial Intelligence Review, 2022 Abstract Causality has been a burgeoning field of research leading to the point where the literature abounds with different components addressing distinct parts of causality. For researchers, it has been increasingly difficult to discern the assumptions they have to abide by in order to glean sound conclusions from causal concepts or methods ...
Vonk, M.C. +3 more
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J. Mach. Learn. Res.50 pages, 5 tables, 11 algorithms, 5 ...
Ehsan Mokhtarian +3 more
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On Incorporating Prior Knowledge Extracted From Large Language Models Into Causal Discovery
IEEE AccessLarge Language Models (LLMs) can reason about causality by leveraging vast pre-trained knowledge and text descriptions of datasets, demonstrating their effectiveness even when data is scarce.
Chanhui Lee +12 more
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Argumentation for Interactive Causal Discovery
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023 Causal reasoning reflects how humans perceive events in the world and establish relationships among them, identifying some as causes and others as effects. Causal discovery is about agreeing on these relationships and drawing them as a causal graph.
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Causal discovery with a mixture of DAGs
Machine Learning, 2022 Causal processes in biomedicine may contain cycles, evolve over time or differ between populations. However, many graphical models cannot accommodate these conditions. We propose to model causation using a mixture of directed cyclic graphs (DAGs), where the joint distribution in a population follows a DAG at any single point in time but potentially ...
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Causal Discovery Evaluation Framework in the Absence of Ground-Truth Causal Graph
IEEE AccessIn causal learning, discovering the causal graph of the underlying generative mechanism from observed data is crucial. However, real-world data for causal discovery is scarce and expensive, leading researchers to rely on synthetic datasets, which may not
Tingpeng Li +5 more
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Pediatric Blood &Cancer, EarlyView.ABSTRACT Background Type 1 plasminogen deficiency (PLGD‐1) is an ultra‐rare autosomal recessive disorder caused by variants in the PLG gene and affects approximately 1.6 individuals per million. The condition is characterized by decreased plasminogen levels and impaired function, resulting in fibrin‐rich lesions on mucous membranes throughout the body.
Charles Nakar +7 more
wiley +1 more source
Bayesian causal discovery for policy decision making
Data & PolicyThis paper demonstrates how learning the structure of a Bayesian network, often used to predict and represent causal pathways, can be used to inform policy decision-making.
Catarina Moreira +6 more
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Personalized Zebrafish Models for Fusion‐Positive Pediatric Sarcomas
Pediatric Blood &Cancer, EarlyView.ABSTRACT Clinical sequencing efforts have revolutionized our approaches to categorizing pediatric cancers in real time. This has dramatically improved our ability to profile pediatric tumors, identify actionable vulnerabilities, and influence clinical care.
Lisa H. Hall +2 more
wiley +1 more source