alterlab-causal-inference

Pass

Audited by Gen Agent Trust Hub on Jul 6, 2026

Risk Level: SAFE
Full Analysis
  • [SAFE]: The skill relies on well-known and verified Python libraries for statistical modeling, including statsmodels, linearmodels, pyfixest, dowhy, econml, and rdrobust. These are standard tools in the data science ecosystem.
  • [SAFE]: No network activity or external data exfiltration patterns were detected. The skill operates locally on provided datasets without attempting to connect to remote servers.
  • [SAFE]: The included Python script scripts/estimator_router.py is a simple, standard-library-only tool used for mapping research designs to their respective estimators and assumptions. It contains no suspicious logic, obfuscation, or dynamic execution patterns.
  • [SAFE]: The instructions emphasize rigorous validation (diagnostics and refutation), which is a best practice for research integrity and prevents the generation of misleading causal claims.
  • [SAFE]: There are no signs of prompt injection, persistence mechanisms, or privilege escalation. The allowed-tools configuration is appropriately scoped to reading files and executing Python code for analysis purposes.
Audit Metadata
Risk Level
SAFE
Analyzed
Jul 6, 2026, 11:26 PM
Security Audit — agent-trust-hub — alterlab-causal-inference