Using Predictions and Marginal Effects to Compare Groups in Regression Models for Binary Outcomes

Methods for group comparisons using predicted probabilities and marginal effects on probabilities are developed for regression models for binary outcomes. Unlike approaches based on the comparison of regression coefficients across groups, the methods we propose are unaffected by the scalar identification of the coefficients and are expressed in the natural metric of the outcome probability. While we develop our approach using binary logit with two groups, we consider how our interpretive framework can be used with a broad class of regression models and can be extended to any number of groups.

Automated summary

This study introduces methods for group comparisons using predicted probabilities and marginal effects on probabilities in regression models for binary outcomes. Unlike traditional approaches, these methods are not affected by the scalar identification of regression coefficients and are expressed in the natural metric of the outcome probability. The interpretive framework developed in this study can be applied to a wide range of regression models and can be extended to any number of groups.