Variance-based sensitivity analysis for weighting estimators result in more informative bounds

Understand

Weighting methods are popular tools for estimating causal effects; assessing their robustness under unobserved confounding is important in practice.

• In the following paper, we introduce a new set of sensitivity models called "variance-based sensitivity models".
• Variance-based sensitivity models characterize the bias from omitting a confounder by bounding the distributional differences that arise in the weights from omitting a confounder, with several notable innovations over existing approaches.
• First, the variance-based sensitivity models can be parameterized with respect to a simple $R^2$ parameter that is both standardized and bounded.