2012/05/09 by Patrik O. Hoyer, Hoyer, Patrik O., Antti Hyttinen +1 · 21 citations
Computer Science · Engineering · Mathematics · #Algorithm #Artificial intelligence #Bayesian Modeling and Causal Inference #Bayesian network #Bayesian probability #Computer science #Directed acyclic graph #Econometrics #FOS: Computer and information sciences #Fault Detection and Control Systems #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Mathematics #Methodology (stat.ME) #Statistical Methods and Bayesian Inference #cs.LG #stat.ME #stat.ML
paper · pdf · doi:10.48550/arxiv.1205.2641
published in arXiv (Cornell University) (Cornell University) · Appears in Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (UAI2009)
arxiv created 2012/05/09 · openalex publication_date 2012/05/09 · arxiv updated 2012/05/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Methods for automated discovery of causal relationships from non-interventional data have received much attention recently. A widely used and well understood model family is given by linear acyclic causal models (recursive structural equation models). For Gaussian data both constraint-based methods (Spirtes et al., 1993; Pearl, 2000) (which output a single equivalence class) and Bayesian score-based methods (Geiger and Heckerman, 1994) (which assign relative scores to the equivalence classes) are available. On the contrary, all current methods able to utilize non-Gaussianity in the data (Shimizu et al., 2006; Hoyer et al., 2008) always return only a single graph or a single equivalence class, and so are fundamentally unable to express the degree of certainty attached to that output. In this paper we develop a Bayesian score-based approach able to take advantage of non-Gaussianity when estimating linear acyclic causal models, and we empirically demonstrate that, at least on very modest size networks, its accuracy is as good as or better than existing methods. We provide a complete code package (in R) which implements all algorithms and performs all of the analysis provided in the paper, and hope that this will further the application of these methods to solving causal inference problems.