Is Causalml Safe?
Causalml — Nerq Trust Score 44.5/100 (D grade). Based on analysis of 2 trust dimensions, it is has notable safety concerns. Last updated: 2026-04-01.
Exercise caution with Causalml. Causalml is a Python package with a Nerq Trust Score of 44.5/100 (D), based on 3 independent data dimensions. It is below the recommended threshold of 70. Security: 65/100. Popularity: 50/100. Data sourced from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-04-01. Machine-readable data (JSON).
Is Causalml safe?
NO — USE WITH CAUTION — Causalml has a Nerq Trust Score of 44.5/100 (D). It has below-average trust signals with significant gaps in security, maintenance, or documentation. Not recommended for production use without thorough manual review and additional security measures.
What is Causalml's trust score?
Causalml has a Nerq Trust Score of 44.5/100, earning a D grade. This score is based on 2 independently measured dimensions including security, maintenance, and community adoption.
What are the key security findings for Causalml?
Causalml's strongest signal is security at 65/100. No known vulnerabilities have been detected. It has not yet reached the Nerq Verified threshold of 70+.
What is Causalml and who maintains it?
| Author | Huigang Chen, Totte Harinen, Jeong-Yoon Lee, Jing Pan, Mike Yung, Zhenyu Zhao |
| Category | pypi |
| Source | N/A |
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Safety Guide: Causalml
What is Causalml?
Causalml is a Python package — Python Package for Uplift Modeling and Causal Inference with Machine Learning Algorithms.
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=causalml
Key Safety Concerns for Python packages
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Trust Assessment
Causalml has a Nerq Trust Score of 44/100 (D) and has not yet reached Nerq trust threshold (70+). This score is based on automated analysis of security, maintenance, community, and quality signals.
Key Takeaways
- Causalml has a Trust Score of 44/100 (D).
- Review carefully before use — below trust threshold.
- Always verify independently using the Nerq API.
Frequently Asked Questions
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Disclaimer: Nerq trust scores are automated assessments based on publicly available signals. They are not endorsements or guarantees. Always conduct your own due diligence.