Treatment effect assessment in observational studies with multi-level treatment and outcome

In observational studies, one of the main difficulties consists in the comparison of treatment effects. In fact, receiving a treatment is not a “purely random” event, and there could be relevant differences between treatment groups. Propensity score is a popular tool to account for this source of bias. However, its use requires a careful modelization of the dependence relationships of the treatment on the covariates. In this work, we consider a general setting with multiple treatments and discrete multi-valued outcome. We propose to estimate the propensity score by using Bayesian Networks and, based on this, we develop an inferential methodology to evaluate the treatments effect. The performance of the proposed approach have been studied through a simulation study with very promising results.