Causal AI Features

  • Causal Discovery

  • Causal Inference

  • Integration with Domain Knowledge

  • Decision-making

An AI expert marking decisions in vertical industries.

Causal analysis delves into reasons and principles behind observed outcome by identifying causal factors, which facilitate business organizations to take proactive actions to intervene desired results. Therefore, to successfully run causal analysis on various datasets will unlock invaluable insights during decision-making process and enhance business outcome.

Automatically establish cause and effect.

R&B’s causality engine is able to perform causal discovery for various datasets in an autonomous way. The engine will automatically, after reasonable effort of preprocessing raw data, discover causal relationships between variables in a hybrid method of unsupervised and supervised learning. A causal graph will be generated to represent different variables (or dataset) and the discovered causal relationships. An example of causal graph for an industrial facility system is shown on the right.

Causal inference.

When the causal discovery is completed the engine will start executing causal inference in which causal factors are investigated to decide how they influence, in another word, cause specific outcomes with quantitative measurements.