Sensitivity analysis, a statistical method crucial for validating inferences across disciplines, quantifies the conditions that could alter conclusions (Razavi et al., 2021). One line of work is rooted in linear models and foregrounds the sensitivity of inferences to the strength of omitted variables (Cinelli & Hazlett, 2019; Frank, 2000). A more recent approach is rooted in the potential outcomes framework for causal inference and foregrounds how hypothetical changes in a sample would alter an inference if such cases were otherwise observed (Frank et al., 2008, 2013; Frank & Min, 2007; Xu et al., 2019). One sensitivity measure is the "Impact Threshold of a Confounding Variable," or ITCV, which generates statements about the correlation of an omitted, confounding variable with both a predictor of interest and the outcome (Frank, 2000). The ITCV index can be calculated for any linear model. The "Robustness of an Inference to Replacement," RIR, assesses how replacing a certain percentage of cases with counterfactuals of zero treatment effect could nullify an inference (Frank et al., 2013). The RIR index is more general than the ITCV index.