Popular GitHub Repositories for Causal Graph

Cross-platform, customizable ML solutions for live and streaming media.

Graph Neural Network Library for PyTorch

Make interlinked notes in private (E2E encrypted), share parts of it to global network of topics with deep AI integration

Python SDK for Agent AI Observability, Monitoring and Evaluation Framework. Includes features like agent, llm and tools tracing, debugging multi-agentic system, self-hosted dashboard and advanced analytics with timeline and execution graph view

Python package built to ease deep learning on graph, on top of existing DL frameworks.

Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

Reliable LLM Memory for AI Applications and AI Agents

Anomaly detection related books, papers, videos, and toolboxes

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

FlowGram是一个基于节点的流程构建引擎，帮助开发者快速创建工作流程，支持固定布局和自由连接布局两种模式。

Translate darknet to tensorflow. Load trained weights, retrain/fine-tune using tensorflow, export constant graph def to mobile devices

Gorgonia is a library that helps facilitate machine learning in Go.

Uplift modeling and causal inference with machine learning algorithms

Build Graph Nets in Tensorflow

links to conference publications in graph-based deep learning

A distributed approximate nearest neighborhood search (ANN) library which provides a high quality vector index build, search and distributed online serving toolkits for large scale vector search scenario.

A collection of important graph embedding, classification and representation learning papers with implementations.

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

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