code-audit

You can audit research code safely by running a strictly read-only static analysis on your R or Python scripts before any execution. This two-phase workflow prevents data corruption by requiring explicit user approval before running code that touches licensed datasets.

A static code audit checks causal inference pipelines for econometric validity issues like clustering errors, fixed effects misconfigurations, and difference-in-differences or instrumental variable pitfalls. It also verifies data integrity and reproducibility to ensure your results are reliable.

Yes, you can review R and Python panel data scripts without executing the code. The first phase of the audit workflow performs a strictly static analysis to identify bugs and data integrity issues in read-only mode before any commands are run.

You get actionable recommendations from an empirical finance code review by generating a severity-based output. The audit provides ordered recommendations that reference specific code lines, helping you systematically fix data integrity and econometric validity issues.

Yes, the two-phase code audit workflow supports scripts handling WRDS, CRSP, WellDatabase, and PLIDA data. It is specifically designed to validate empirical research pipelines that process licensed or sensitive panel datasets safely.

You should use a read-only code audit when your empirical script handles licensed or sensitive datasets. Running a static analysis first prevents wrong conclusions from buggy or unsafe research pipelines by enforcing a structured validation check before execution.