KonFound

My role: Collaborator (PI: Dr. Kenneth Frank)

Intro

Unmeasured confounding is fundamental to social and health sciences. Even after controlling for the most likely alternative explanations for an inferred effect, there may be some alternative explanation(s) that cannot be ruled out with observed data.

Generally, the first response is to develop the best models that maximally leverage the available data. After that, sensitivity analyses can inform discourse about an inference by quantifying the unobserved conditions necessary to change the inference.

This project advances sensitivity analysis tools so that applied researchers can use such tools to better communicate their findings.

Our approaches could be applied to linear regression, logistic regression, mediation, as well as other research designs.

Tools

• R-shiny
• R package
• Stata package