Summary

In this chapter, we explored sensitivity analysis, a crucial tool in causal inference that helps us evaluate how stable our findings are under different assumptions. We saw how this technique, pioneered by researchers such as Cornfield, is especially valuable in observational studies where uncertainties are common. Sensitivity analysis not only makes our causal claims stronger but also shows us where to focus future research by revealing the impact of factors we can’t observe directly. While it’s incredibly useful, we noticed it’s not used as widely as it should be across different fields of study. This gap highlights how important it is for more researchers to adopt this method. By using sensitivity analysis more often, we can make our understanding of cause and effect more robust and detailed, leading to better-informed policies and scientific investigations. As you continue your research journey, consider how incorporating sensitivity analysis could...