Causal Safety Engine

Industrial-grade causal discovery and safety certification engine

Overview

Causal Safety Engine is an industrial-grade engine for causal discovery and certification of reliable insights, designed for enterprise environments, regulated AI systems, and deep-tech startups that require:

• causality (not correlation)
• robustness
• multi-run stability
• auditability
• API-based integration

The system is designed as a causal safety layer on top of existing AI/ML pipelines.

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Design Principle: Causal Silence

When causal identifiability is insufficient, the engine intentionally produces no insights. Silence is treated as a correct and safe outcome, not a failure.

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Intervention Safety & Action Blocking

The Causal Safety Engine never authorizes interventions by default.

Causal discovery and causal action are treated as strictly separate phases. Even when exploratory or tentative causal signals exist, the engine:

• does not recommend actions
• does not generate intervention plans
• does not expose “what-to-do” outputs

Interventions are explicitly blocked unless all of the following conditions are met:

• causal identifiability is satisfied
• robustness and stability tests pass
• no safety or silence gate is triggered
• the run is explicitly marked as intervention-enabled

When causal certainty is insufficient, the correct and safe behavior is causal silence: no insights promoted, no actions suggested, no downstream activation.

This design prevents unsafe automation, decision leakage, and premature causal deployment in high-stakes or regulated environments.

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Key Capabilities

✔ True Causal Discovery

• Identifies genuine causal relationships
• Rejects spurious correlations
• Handles confounders and common causal biases

✔ Causal Safety & Guardrails

• Explicit rejection of:

• Simpson’s paradox

• collider bias

• data leakage

• spurious time trends

• Safety-first default behavior (no false positives by design)

✔ Robustness & Stability

• Automated testing for:

• stress scenarios

• multi-run stability

• reproducibility

• Consistent outputs under data perturbations

✔ Audit & Certification Ready

• Every run is:

• isolated

• hashed

• traceable

• Artifacts are preserved for verification and compliance

✔ API-First Architecture

• Engine exposed as a service
• Easy integration into enterprise pipelines
• Ready for industrial deployment

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Repository Structure

IMPLEMENTATION/
pcb_one_click/
demo.py              # core causal engine
data.csv             # example dataset
stress_test/         # safety & stability tests

api/
causal_api_main.py     # production-grade API

runs/
<run_id>/
data.csv
out/
edges.csv
insights_*.csv

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Safety & Certification Pipeline

The project includes a fully automated CI pipeline with:

• Functional engine tests
• Causal safety stress tests
• Multi-run stability tests
• API health and integration tests

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Project Status

• Engine: production-ready reference implementation
• API: production-grade architecture
• CI/CD: fully automated
• Safety & stability: certified via tests

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Partnerships & Licensing

This project is designed for:

• industrial partnerships
• OEM integration
• startup studio collaboration

For partnership, licensing, or deployment discussions, please contact the project owner.