Overview We are thrilled to announce the release of deep_causality_physics, a new crate designed to accelerate complex physics simulations, advanced engineering, and scientific computing in Rust.
While Rust has an exploding ecosystem for linear algebra (ndarray, nalgebra) and game physics (rapier), there has been a gap in high-fidelity theoretical physics. We built deep_causality_physics to fill this gap, providing a comprehensive collection of kernels spanning from Quantum Mechanics to General Relativity.
Overview The DeepCausality project is proud to announce deep_causality_haft (Higher-Order Abstract Functional Traits), a standalone crate that brings rigorous functional programming abstractions to Rust. HAFT serves as the mathematical engine room for DeepCausality, enabling the complex, context-aware reasoning capabilities of the core library.
While Rust’s type system is incredibly powerful, it famously lacks native support for Higher-Kinded Types (HKTs). This post explores why the DeepCausality needed them, how we emulated them using the Witness Pattern, and how this enables Type-Encoded Effect Systems.
Overview The DeepCausality project announces the release of deep_causality_discovery, a new crate designed to bridge the gap between raw data and causal models. This release introduces the Causal Discovery Language (CDL), a Domain Specific Language (DSL) that allows researchers and engineers to define robust, type-safe pipelines for extracting causal insights from observational data.
The Problem: Causal Discovery Building a causal model requires knowing structure: which variables influence which, and how? In complex systems, such as in finance, biology, or systems engineering, this structure is rarely obvious.
Overview We are excited to introduce the Causal Monad, a powerful new core feature in DeepCausality that radically simplifies how you write complex causal logic.
If you have ever built a complex data processing pipeline, you know the pain: you start with a simple value, but soon you are juggling error handling, audit logging, configuration contexts, and mutable state across a dozen function calls buried under boilerplate.
The Causal Monad solves this by abstracting away the plumbing. It allows you to chain causal operations together while automatically handling:
Overview In scientific computing, we often work in silos. We use one library for Tensors (linear algebra), another for Graphs (topology), and yet another for Geometric Algebra or Quantum Mechanics. Bridging these worlds usually involves glue code, manual type conversions, and a loss of semantic meaning. In practice, that means solutions derived from a numerical solver are exported as a file, and then imported into another math library for further processing.
Overview The DeepCausality project announces the introduction of Uncertain<T> and the MaybeUncertain<T> types within the deep_causality_uncertain crate. These first-class types provide a robust way to model and propagate uncertainty, ensuring your causal models are reliable.
💡 The Problem with Certainty in an Uncertain World Traditional programming often forces us to represent inherently uncertain data with discrete, “certain” types like f64 or bool. This approach, however, hides the underlying variance and can lead to:
Overview The DeepCausality project is proud to announce the release of DeepCausality 0.11, an update that removes all external dependencies from the default build. This release enhances security, portability, and compilation speed, delivering a self-contained and robust library for computational causality.
🚀 Highlights in 0.11 Zero External Dependencies: The core library has no external dependencies by default. 100% Safe Rust: The entire codebase contains zero unsafe code blocks. Zero Macros: No procedural macros are used in the core library (except for testing). New Internal Crates: deep_causality_rand, deep_causality_num, and deep_causality_tensor. Blazing-Fast Compilation: Full mono-repo builds complete in seconds. 💡 Zero Dependencies by Default The entire DeepCausality codebase, as it is now, compiles by default without any external dependencies.
Sept 19, 2025, JetBrains, the premier software development IDE provider, continues to grant a free all-product license to the DeepCausality project. The project team expresses its sincere gratitude towards JetBrains continuous commitment to the open source community and to the success of the DeepCausality project.
JetBrains Website OSS Community support Blog DeepCausality Website GitHub Blog About DeepCausality is a hyper-geometric computational causality library that enables fast and deterministic context-aware causal reasoning in Rust. Please give us a star on GitHub.
Overview The DeepCausality project announces the release of DeepCausality 0.9 that adds support for the Effect Ethos, a programmable ethos for dynamic adaptive causal inference.
Problem DeepCausality was designed from the ground up to model a dynamic world that is constantly in motion. Its reasoning modes allow us to build systems that use dynamic reasoning models to react to evolving data streams. With adaptive reasoning, they can dynamically alter their own reasoning pathways, choosing the best strategy based on the current context.
Overview When the decision was made to rewrite UltraGraph from scratch for the DeepCausality project, the goal was clear: create a high-performance, memory-efficient, and adaptable graph data structure that is tailor-made for DeepCausality. One of the key requirements was the support for both fast mutation as required by the graph-based context and fast graph traversal as required by the causal reasoning engine. Seasoned engineers will spot immediately the conundrum to solve because data structures that are fast to mutate are fundamentally different from data structures that are fast to analyze. This subsequent quest resulted in deep insights into the trade-offs between flexibility and performance. Here are the key lessons learned along the way.
