  This paper proposes a new statistical approach for assessing treatment effect
using Bayesian Networks (BNs). The goal is to draw causal inferences from
observational data with a binary outcome and discrete covariates. The BNs are
here used to estimate the propensity score, which enables flexible modeling and
ensures maximum likelihood properties, including asymptotic efficiency. %As a
result, other available approaches cannot perform better. When the propensity
score is estimated by BNs, two point estimators are considered - H\'ajek and
Horvitz-Thompson - based on inverse probability weighting, and their main
distributional properties are derived for constructing confidence intervals and
testing hypotheses about the absence of the treatment effect. Empirical
evidence is presented to show the goodness of the proposed methodology on a
simulation study mimicking the characteristics of a real dataset of prostate
cancer patients from Milan San Raffaele Hospital.
