DeepCausality is a hyper-geometric computational causality library that enables fast and deterministic context-aware causal reasoning over complex multi-stage causality models. Deep Causality adds only minimal overhead and thus is suitable for real-time applications without additional acceleration hardware.
DeepCausality is written in Rust with safety, reliability, and performance in mind.
DeepCausality provides recursive causal data structures that concisely express arbitrary complex causal structures.
DeepCausality enables context awareness across data-like, time-like, space-like, spacetime-like entities stored within (multiple) context-hyper-graphs.
DeepCausality simplifies modeling of complex tempo-spatial patterns.
DeepCausality comes with Causal State Machine (CSM).
DeepCausality is a sandbox-stage project of LF AI & Data Foundation.
Contributed by Emet-Labs in August 2023.