Application of high-throughput data from microarray experiments is a promising way of finding regulatory relationships between genes. Recently, a causal model approach was presented for learning gene regulatory networks from microarray data. The paper investigates and extends this model by identifying the way in which the causal structure can be best exploited computationally to make inference faster and closer to the underlying model. We examine the influence of various hypotheses about the sub-model space on the discovery of causal models. We develop scoring algorithms to learn V-structure, Y-structure and the Markov blanket of every node, by the efficient application of conditional independence (CI) tests of increasing orders on the data. Data from artificial networks as well as "real-world" S.cerevisiae cellcycle measurements (Spellman et al) were used to evaluate the confidence in our scoring algorithms. The Markov blanket algorithm demonstrated better structural correctness, computational efficiency along with accuracy improvement compared to V-structure and Y-structure learning algorithms.