Make beautiful directed acyclic graphs (DAGs)

🛸 The Directed Prediction Index (DPI): Quantifying Relative Endogeneity for Causal Inference from Observational Data.

C++ implementation of some of the most well-known graph algorithms in the simplest way. Mostly in non-OOP style because the algorithms itself and its performance were the main points.

DAGAF is a novel generative framework that simultaneously discovers causal structures and produces high-fidelity synthetic tabular data.

The aim of this project is to design and implement a dynamic algorithm to verify and enumerate safe path when a new edge is inserted or flow is increased along a path.

A graphical causal modeling tool tailored for physical therapy research and clinical reasoning. Part of the Clinical Inquiry Network ecosystem.

Replication of a scientific paper in python, focused on acyclic graphs, post-treatment bias and fixed effects. Enriched with resampling and linear model selection techniques.

A humble attempt at langgraph in rust

Implementations for various graph algorithms.