Understanding that causal inference requires explicit assumptions and methodological rigor beyond traditional statistical correlation analysis.

Emphasize sensitivity analysis and assumption stress-testing as standard practices to evaluate how violations may impact casual estimates.

Robust causal relationships should demonstrate consistency across different analytical approaches, subsamples, and bootstrap replicates.

Effective causal analysis requires combining statistical methods with domain knowledge to distinguish plausible from implausible causal mechanisms.