Researchers identified over 8,000 causal relationships between diseases using scientific literature and real-world patient ...

There are two approaches to causal AI that are based on long-known principles: the potential outcomes framework and causal graph models. Both approaches make it possible to test the effects of a ...

One of the approaches used in Causal AI, based on long-understood principles, is known as causal graph modeling. A Causal graph is a pictorial graph made up of collections of variables (nodes), linked ...

The rigorous data analysis is captured in causal graphs that can be reliably used in high-quality, regulatory-grade life science. Ultimately, causal AI makes GenAI more trustworthy and compliant ...

At the heart of Alembic’s breakthrough is a new type of graph neural network that acts as a causal reasoning engine. This AI brain ingests data from a wide range of enterprise systems ...

Alembic is the first to precisely trace and prove the results of marketing programs and is the first to apply composite AI, causal AI, a graph neural network and advanced contact-tracing ...

SWIGs provide a visual representation of causal effects to make the estimand of the trial explicit. In other words, the key distinction between two types of graphs, DAGs and SWIGs, lies in the ...

LONDON--(BUSINESS WIRE)--causaLens, the London deep tech company and pioneer of Causal AI, today announced the launch of decisionOS, the first operating system using cause-and-effect reasoning for ...