This repository is the `lingam` Python package, a comprehensive, research-oriented library for causal discovery from observational data using LiNGAM (Linear Non-Gaussian Acyclic Model) and its numerous extensions. It implements over 20 algorithms, including DirectLiNGAM, ICALiNGAM, VAR-LiNGAM, RCD, CAM-UV, RESIT, LiM, mLiNGAM, GroupLiNGAM, MultiGroup variants, and more. The library provides a consistent API where each algorithm exposes `fit()` on a data matrix, followed by properties like `causal_order_` and `adjacency_matrix_`, and all support bootstrap-based stability analysis via a shared `BootstrapMixin`. Key contributions include handling of latent confounders, time series, missing data, mixed data types, and multiple datasets, alongside utilities for causal effect estimation, visualization, model evaluation, and synthetic data generation. The code is well-structured, uses standard scientific Python dependencies (numpy, scipy, scikit-learn, graphviz), and includes a thorough test suite and extensive Jupyter notebook tutorials.
Kanaries/Rath is an open-source augmented analytics BI platform that automates exploratory data analysis, causal discovery, and dashboard creation. It is a full-stack application with a React/TypeScript frontend (Vite, MobX, shadcn/ui) and multiple Python microservices (causal inference, narrative insight generation, database connectors). The system ingests data from files, databases (PostgreSQL, ClickHouse, MySQL, etc.), and REST APIs, then provides automated visualization (mega-automation), semi-automated exploration, causal analysis (PC, FCI, GES, XLearner), and interactive dashboard building. It also includes custom Vega renderers (WebGL, Painter) for high-performance chart rendering and an Electron desktop wrapper. While primarily an application, it contains significant data research components such as causal discovery algorithms, statistical insight detection, and text pattern extraction, making it a hybrid tool for both data exploration and advanced analytics.