Abstract: Directed acyclic graphs (DAGs) are central to science and engineering applications including causal inference, scheduling, and the automated design of neural architectures. In this work, we ...

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.

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 ...

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 ...

Graph theory is an integral component of algorithm design that underlies sparse matrices, relational databases, and networks. Improving the performance of graph algorithms has direct implications to ...