In structural learning of directed acyclic graphs (DAGs), testing conditional in dependence plays an important role.Unfortunately, independence test becomes low efficient conditionally on a large set of discrete variables.We propose an approach for testing conditional independence via propensity scores and apply it to structural learning of DAGs.