Title: Have directed acyclic graphs (DAGs) fulfilled their promise in epidemiology and health research? Abstract: Causal directed acyclic graphs (DAGs) are among the most widely used causal diagrams.

Abstract: The stack number of a directed acyclic graph G is the minimum k for which there is a topological ordering of G and a k-coloring of the edges such that no two edges of the same color cross, i ...

Aim Causal inference relies on correct background knowledge, which epidemiologists generally understand to come from academic experts. Our community-engaged study augments scientific domain knowledge ...

The term evidence-based medicine, coined by Dr. Guyatt in 1991 (1), describes the practice of medicine rooted in the best available scientific evidence (2). Since its inception, evidence-based ...

Evidence-based Directed Acyclic Graphs (DAGs) are effective tools to comprehensively visualize complex causal and biasing pathways in pharmacoepidemiologic research in rheumatology. This paper ...

The number of female filmmakers dropped to 8.1 percent this year from 13.4 percent in 2024, according to a study from the University of Southern California. By Michaela Towfighi Among the top 100 ...

Python package for Causal Discovery by learning the graphical structure of Bayesian networks. Structure Learning, Parameter Learning, Inferences, Sampling methods.

Directed Acyclic Graphs with a variety of methods for both Nodes and Edges, and multiple exports (NetworkX, Pandas, etc). This project is the foundation for a commercial product, so expect regular ...

Abstract: A directed acyclic graph (DAG) is a specialized form of directed graph used extensively for analyzing relationships and dependencies across various domains. However, spectral analysis—a core ...