Introduction to sensitivity analysis
Sensitivity analysis stands as a methodological approach within the realm of causal inference studies that focuses on evaluating how variations in the underpinning assumptions of a model may influence the conclusions drawn about causal relationships. This analytical technique plays a pivotal role in appraising the stability of conclusions that are inherently predicated on assumptions that might not be directly verifiable through the data at hand. By identifying and quantifying the extent to which the outcomes are affected by changes in these foundational assumptions, sensitivity analysis offers a more nuanced understanding of the level of confidence that researchers can place in their causal inferences.
Why do we need sensitivity analysis?
That’s a good question. Why do we need it? Well, sensitivity analysis primarily aims to evaluate the reliability of outcomes or conclusions across various scenarios or assumptions, particularly in...
