2019/10/01 by Mohammad Ali Javidian, Javidian, Mohammad Ali, Marco Valtorta +3
Computer Science · Decision Sciences · #Advanced Graph Neural Networks #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Multi-Criteria Decision Making
paper · pdf · doi:10.48550/arxiv.1910.01067
openalex publication_date 2019/10/01 · openalex created_date 2022/07/28 · openalex updated_date 2026/07/28
This paper deals with multivariate regression chain graphs (MVR CGs), which\nwere introduced by Cox and Wermuth [3,4] to represent linear causal models with\ncorrelated errors. We consider the PC-like algorithm for structure learning of\nMVR CGs, which is a constraint-based method proposed by Sonntag and Pe ~na in\n[18]. We show that the PC-like algorithm is order-dependent, in the sense that\nthe output can depend on the order in which the variables are given. This\norder-dependence is a minor issue in low-dimensional settings. However, it can\nbe very pronounced in high-dimensional settings, where it can lead to highly\nvariable results. We propose two modifications of the PC-like algorithm that\nremove part or all of this order-dependence. Simulations under a variety of\nsettings demonstrate the competitive performance of our algorithms in\ncomparison with the original PC-like algorithm in low-dimensional settings and\nimproved performance in high-dimensional settings.\n