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 🔍

Logistic optimization: Delivery drivers location optimization with Causal Inference

R code for causal graph animations