Tutorials on Causal Inference and pgmpy

Algorithms for quantifying associations, independence testing and causal inference from data.

CausIL is an approach to estimate the causal graph for a cloud microservice system, where the nodes are the service-specific metrics while edges indicate causal dependency among the metrics. The approach considers metric variations for all the instances deployed in the system to build the causal graph and can account for auto-scaling decisions.

A curated list of amazingly awesome things regarding Graph Structure Learning.

🔎 Benchmarking Framework for Extendability of Causal Graphs 🔍

🧠 R2T Prototype: An LLM pre-trained on causal graphs (not just text) to build provably faithful step-by-step reasoning.

Logistic optimization: Delivery drivers location optimization with Causal Inference

R code for causal graph animations

Implémentation d’un système d’IA Explicable (XAI) basé sur les explications contrastives bi-factuelles, avec optimisations algorithmiques et interface graphique CausaLytics.

🔍 Enhance reasoning accuracy with the Reflective Reasoning Transformer, leveraging causal reasoning graphs for better dynamic reasoning performance.

🧠 Implement a bi-factual contrastive explanation system for AI decisions, enhancing understanding through formal definitions and optimized algorithms.