Assessing multi-treatment 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.