Researchers Map Causal Links from Probability Data

Causal relationships between discrete variables can be reliably mapped from observational data alone. A new method using fully connected tensor networks recovers the ‘moral graph’, an underlying representation of these connections, directly from probability distributions. The method accurately identifies causal links by reconstructing underlying patterns without relying on traditional statistical tests or searching through numerous possibilities. This approach offers an alternative direction for learning how structures emerge from increasingly complicated datasets. Fully connected tensor networks decipher relationships within complex datasets; they function like interconnected building blocks modelling…