Assessing Multitreatment Effects for Discrete Observational Data

This paper focuses on detecting the existence of a treatment effect when comparing multiple treatments using discrete observational data. A new test statistic, based on a divergence measure, is introduced, and its theoretical properties are obtained. Stochastic dominance tests are also proposed, illustrating relevant practical cases of treatment‐level effects ordering patterns. Numerical simulations show the effectiveness of the proposed tests, in terms of both significance level and power. A clinical application indicates that our methods can significantly enhance the evaluation of multitreatments in real‐world scenarios using observational data.