# Orxaq

**Causal intelligence operating system for credit risk.**

Orxaq replaces correlation-based credit risk models with causal models built on directed acyclic graphs. Every edge in the World DAG represents a causal mechanism — not a statistical association. This enables interventional reasoning, counterfactual analysis, and regulatory-compliant stress testing that correlation models cannot provide.

## Why Causal?

| Capability | Correlation Model | Causal Model (Orxaq) |
|-----------|------------------|---------------------|
| "Why did this borrower default?" | Feature importance (approximate) | Causal path trace (exact) |
| Stress testing unseen scenarios | Extrapolation from training data | Intervention on DAG nodes |
| Fair lending | Disparate impact (statistical) | Direct vs. indirect effects (causal) |
| CECL lifetime loss | Curve fitting | Mechanism-based propagation |
| SR 11-7 conceptual soundness | Goodness-of-fit | Economic theory encoded as DAG |

## Getting Started

```bash
pip install orxaq
orxaq status
orxaq credit ecl
orxaq serve
```

See the [Installation](getting-started/installation.md) and [Quick Start](getting-started/quickstart.md) guides.

## Architecture

Orxaq uses a five-ring architecture where each ring depends only on lower rings:

```
Ring 5: Experiences    CLI, Dashboard, API Server
Ring 4: Orchestration  Conductor, Workers, Scheduling
Ring 3: Intelligence   PC, GES, Consensus, D-Separation
Ring 2: Data Fabric    Connectors, Schema, Quality Gates, Lineage
Ring 1: Kernel         WorldDAG, Types, Audit Log, Crypto
```

Read more in [Architecture Overview](architecture/overview.md).
