Effect Modification and Design Sensitivity in Observational Studies

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Imposing Minimax and Quantile Constraints on Optimal Matching in Observational Studies (2017, background): “For instance, this can be advantageous when studying effect modification involving a few covariates (Hsu, Small, and Rosenbaum 2013; Hsu et al. 2015) or when seeking reduced sensitivity to unmeasured biases by reducing withinpair heterogeneity of response (Rosenbaum 2005).”
On Sensitivity Value of Pair-Matched Observational Studies (2017, background): “Selecting subpopulations In presence of effect modification (interaction between treatment and covariates), Hsu et al. (2013) discovered an interesting phenomenon that the investigator might prefer to test on subgroups with larger effects because they are less sensitive to hidden bias.”
Sharp Sensitivity Analysis for Inverse Propensity Weighting via Quantile Balancing (2021, methodology): “In alternative sensitivity models, they have also been observed by [47, 22], [11], and others.”