Beyond principal ignorability: Nonparametric sensitivity bounds for principal stratification

2026-06-01

AI Summary

This study addresses the challenge of identifying principal causal effects under potential violations of the untestable principal ignorability assumption. The authors propose the first nonparametric sensitivity analysis framework that does not rely on this assumption, introducing unbounded sensitivity parameters based on the relative risks of selection and outcomes induced by an unobserved confounder. Within this framework, they derive sharp nonparametric bounds for principal causal effects, establish Cornfield-type conditions, and define a principal E-value that quantifies the minimum strength of unmeasured confounding required to nullify the estimated causal effect. The methodology is further extended to generalized principal causal effects and pairwise comparisons over product spaces, offering a robust approach to assessing causal inference sensitivity in settings where principal ignorability may not hold.

Abstract

Principal stratification is an effective framework addressing intermediate variables in causal inference. However, point identification of the principal causal effects (PCEs) often requires the untestable principal ignorability (PI) assumption. This article develops a nonparametric sensitivity analysis framework for evaluating PI violations. We introduce a margin-free bounding factor parameterized by the selection and outcome relative risks of an unmeasured confounder. Using this bounding factor, we derive sharp nonparametric bounds for each PCE. We prove that these bounds nest within the worst-case nonparametric bounds with and without the monotonicity assumption. We then discuss Cornfield-type conditions and principal E-values that quantify the minimum joint magnitude of unmeasured confounding required to nullify the target PCE. Furthermore, we generalize this methodology to principal generalized causal effects, extending the sensitivity bounds and falsification thresholds to the recent pairwise comparison estimands evaluated over a product space.

Problem

• principal stratification
• principal ignorability
• unmeasured confounding
• causal inference
• sensitivity analysis

Innovation

• principal stratification
• nonparametric bounds
• unmeasured confounding
• sensitivity analysis
• principal E-value