vaijira / deep_causality

Hyper-geometric computational causality library for Rust

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.

🤔 Why DeepCausality?

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)

📚 Docs

• API Docs
• Documentation
• Changelog
• Slides

🚀 Install

In your project folder, just run in a terminal:

cargo add deep_causality

How to run the example code

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:

# make sure you're in the project root folder
cd deep_causality

# CSM (Causal State Machine)
cargo run --release --bin example-csm

# CTX (Context) 
cargo run --release --bin example-ctx

# Smoking inference
cargo run --release --bin example-smoking

📦 Sub-Crates

• Datastructures
• Ultragraph
• Macros

⭐ Usage

Docs:

• Introduction
• Architecture
• Background
• Concepts

Code:

• Benchmarks
• Example code
• Tests

🛠️ Cargo & Make

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:

• clippy
• nextest
• outdated
• udeps
• audit
• llvm-cov

    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.

👩‍👩‍👧‍👦 Community

• Charta
• Code of Conduct
• Support

👨‍💻👩‍💻 Contribution

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:

• Code of Conduct
• Contributing
• Release

🙏 Credits

The project took inspiration from several researchers and their projects in the field:

• Judea Pearl at UCLA
• Lucien Hardy at the Perimeter Institute
• Kenneth O. Stanley at OpenAI
• Ilya Shpitser at Johns Hopkins University
• Miguel Hernan, Causal Lab at Harvard University
• Elias Bareinboim at Columbia University
• Causality and Machine Learning at Microsoft Research
• Causal ML at uber.

Parts of the implementation are inspired by:

• Differentiable Types
• Extension Trait
• Storage API
• gTime: Time Graph

Finally, inspiration, especially related to the hypergraph structure, was derived from reading the Quanta Magazine.

📜 Licence

This project is licensed under the MIT license.

👮️ Security

For details about security, please read the security policy.

💻 Author

• Marvin Hansen.
• Github GPG key ID: 369D5A0B210D39BC
• GPG Fingerprint: 4B18 F7B2 04B9 7A72 967E 663E 369D 5A0B 210D 39BC