Is Cai Causal Effects Safe?

Cai Causal Effects — Nerq Trust Score 46.2/100 (D grade). Score based on 2 independent trust signals. Last analyzed: 2026-03-25

Cai Causal Effects is a Python package with a Nerq Trust Score of 46.2/100 (D), based on 3 independent data dimensions. Last analyzed: 2026-03-25 Security: 90/100. Popularity: 0/100. Data sourced from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-03-25. Machine-readable data (JSON).

Safety Guide: Cai Causal Effects

What is Cai Causal Effects?

Cai Causal Effects is a Python package — Causal effect estimation..

How to Verify Safety

Run pip audit or safety check. Review on PyPI for download stats.

You can also check the trust score via API: GET /v1/preflight?target=cai-causal-effects

Key Safety Concerns for Python package

When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.

Measured Signals

Cai Causal Effects has a Nerq Trust Score of 46/100 (D). This score is a composite of automated measurements of security, maintenance, community, and quality signals.

Key Takeaways

• Cai Causal Effects has a Trust Score of 46/100 (D).
• The score is a measured composite — it is not a suitability judgment. Evaluate the individual signals against your own requirements.
• Query the current measured values via the Nerq API.

Disclaimer: Nerq trust scores are automated measurements based on publicly available signals. They are not endorsements, verdicts, or guarantees of suitability. Always evaluate the signals against your own requirements.