Robust Sensitivity Analysis for Inverse Probability Weighting Estimation under Non-Overlap

Music City Center 

Sensitivity analysis is essential for evaluating the robustness of causal conclusions in observational studies, particularly when key assumptions such as no unmeasured confounding and overlap may be violated. In this paper, we propose a robust marginal sensitivity model that accounts for the potential heterogeneity in unmeasured confounding strength and allows for non-overlap between treated and control units. We focus particularly on overlap-weighted average treatment effect that can better accommodate extreme treatment probabilities, and construct confidence intervals for the average treatment effect under given constraints on the violation of unconfoundedness and overlap. Our analysis utilizes constraints from the covariate balancing property of the true propensity score, and the proposed inference is applicable to general Z-estimation with overidentified equations and unmeasured variables.

Causal inference

Sensitivity analysis

Unmeasured confounding

Non-Overlap

Overlap-weighted average treatment effect 

International Chinese Statistical Association