title: Using Triplet Ordering Preferences for Estimating Causal Effects in the Analysis of Gene Expression Data.
abstract: Triplet ordering preferences are used to perform Monte Carlo sampling of the posterior causal orderings originating from the analysis of gene-expression experiments involving observation as well as, usually few, interventions, like knock-outs. The performance of this sampling approach is compared to a previously used sampling via pairwise ordering preference as well as to the sampling of the full posterior distribution. For a fair comparison, the latter approach is restricted to twice the numerical effort of the triplet-based approach. This is done for artificially generated causal, i.e., directed acyclic graphs (DAGs) and for actual experimental data taken from the ROSETTA challenge. The sampling using the triplets ordering turns out to be superior to both other approaches.
authors.affiliation: Institut für Physik, Universität Oldenburg, 26111 Oldenburg, Germany.
authors.forename: Alexander K
authors.identifier: http://orcid.org/0000-0001-6865-5474
authors.initials: AK
authors.lastname: Hartmann
authors.affiliation: LPMA, CNRS 7599, Université Pierre et Marie Curie, Paris, France.
authors.forename: Grégory
authors.initials: G
authors.lastname: Nuel
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D030541:Databases, Genetic; D015870:Gene Expression; D016013:Likelihood Functions; D008957:Models, Genetic; D009010:Monte Carlo Method; D012372:ROC Curve