The reasoning behind decisions is explainable and easily understood by humans. This is because the AI follows a deterministic approach, based on a causal graph that learns and updates in real time.

There are two ways to determine a causal graph: 1) expert domain knowledge and 2) causal discovery algorithms. We will focus on the former for this manufacturing application. For Causal AI to work in ...

Causal reasoning engine drives deterministic AI At the heart of Alembic’s breakthrough is a new type of graph neural network that acts as a causal reasoning engine.

Recently, Quantitative Biology published an approach entitled "Gene Regulatory Network Inference based on Causal Discovery Integrating with Graph Neural Network", that leverages graph ...

Recent clinical trials in oncology have used increasingly complex methodologies, such as causal inference methods for intercurrent events, external control, and covariate adjustment, posing challenges ...