An average-case sensitivity analysis for unmeasured confounding

Understand

Sensitivity analysis for the unconfoundedness assumption is crucial in observational studies.

• For this purpose, the marginal sensitivity model gained popularity recently due to good interpretability and mathematical properties.
• However, most existing models only consider a worst-case parameter that bounds the logit difference between the observed and full data propensity scores, which may not fully capture the extent of unmeasured confounding.
• We propose a new sensitivity model that is parameterized by the second moment of the propensity score ratio, requiring only the average strength of unmeasured confounding to be bounded.