What you will learn

• 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.