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, andrdrobust. 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.pyis 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-toolsconfiguration is appropriately scoped to reading files and executing Python code for analysis purposes.
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