DeepCausality is the first hyper-geometric computational causality library for Rust 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.
How is deep causality different from deep learning?
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Free of the independent and identically distributed data (IID) assumption.
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Deterministic and explainable causal reasoning.
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Reasoning over causal collection, graph, or hyper-graph structure supported.
A focus of this repository is to build state of the art computational causality for Rust
We aim to build an opensource community that encourages collaboration and learning of computational causality and seeks to explore novel applications. We believe that such a goal is in line with LF AI&Data’s mission .
Possible collaboration opportunities with current LF AI hosted projects: (https://lfai.foundation/projects/)
DeepCausality provides fundamental causal datastructures and algorithms and as such is open to collaboration in many different fields. Potential projects for colleaboration might be:
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1chipML
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ForestFlow
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Xtreme1
YES
GitHub Issues: https://github.com/deepcausality-rs/deep_causality/issues
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Apache-2.0, MIT licenses found
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PetGraph
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criterion.rs
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rand
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Marvin Hansen, [email protected], Emet-Labs, 1 year
1 contributor, Emet-Labs
Did the project achieve any of the CII best practices badges? A different badge is required depending on the requested incubation level.
NO. Working on it.
Do you have any specific infrastructure requests needed as part of hosting the project in the LF AI?
NO
Project governance - Do you have a working governance model for the project? Please provide URL to where it is documented, typically GOVERNANCE.md.
All pull requests can be reviewed by any member of the community and needs to be approved by at least one maintainer to be merged.
We also have our contributions guidelines: