Web: https://deepcausality.com
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 hosted as a sandbox project in the LF AI & Data Foundation.
1) DeepCausality is written in Rust with safety, reliability, and performance in mind. 2) DeepCausality provides recursive causal data structures that concisely express arbitrary complex causal structures. 3) DeepCausality enables context awareness across data-like, time-like, space-like, spacetime-like entities stored within (multiple) context-hyper-graphs. 4) DeepCausality simplifies modeling of complex tempo-spatial patterns. 5) DeepCausality comes with Causal State Machine (CSM)
In your project folder, just run in a terminal:
bash
cargo add deep_causality
```bash git clone https://github.com/deepcausality-rs/deep_causality.git
cd deep_causality
make example ```
You can also run the example code from the project root with cargo:
```bash
cd deep_causality
cargo run --release --bin example-csm
cargo run --release --bin example-ctx
cargo run --release --bin example-smoking ```
Docs: * Introduction * Architecture * Background * Concepts
Code: * Benchmarks * Example code * Tests
Cargo works as expected, but in addition to cargo, a makefile exists that abstracts over several additional tools you may have to install before all make commands work:
bash
make build Builds the code base incrementally (fast).
make bench Runs all benchmarks across all crates.
make check Checks the code base for security vulnerabilities.
make coverage Checks test coverage and generates a html report.
make example Runs the code examples.
make fix Auto-fixes linting issues as reported by cargo and clippy.
make test Runs all tests across all crates.
Contributions are welcomed especially related to documentation, example code, and fixes. If unsure where to start, open an issue and ask. For more significant code contributions, please run make test and make check locally before opening a PR.
Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in deep_causality by you, shall be licensed under the MIT license without additional terms or conditions.
For details:
The project took inspiration from several researchers and their projects in the field:
Parts of the implementation are inspired by:
Finally, inspiration, especially related to the hypergraph structure, was derived from reading the Quanta Magazine.
This project is licensed under the MIT license.
For details about security, please read the security policy.