Causal Structure Learning by Using Intersection of Markov Blankets
Not on the map. Why?
Abstract (arXiv)
In this paper, we introduce a novel causal structure learning algorithm called Endogenous and Exogenous Markov Blankets Intersection (EEMBI), which combines the properties of Bayesian networks and Structural Causal Models (SCM). Furthermore, we propose an extended version of EEMBI, namely EEMBI-PC, which integrates the last step of the PC algorithm into EEMBI.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 43 references that are in the map. First posted on arXiv 1 Jul 2023.
Builds on
14 of 43 references.
References · 14
- 2020Denoising Diffusion Probabilistic Modelscited by 21,076
- 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
- 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,576
- 2019An Introduction to Variational Autoencoderscited by 1,000
- 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 752
- 2013CAM: Causal additive models, high-dimensional order search and penalized regressioncited by 178
- 2013Causal Inference and Causal Explanation with Background Knowledgecited by 170
- 2013Causal Inference in the Presence of Latent Variables and Selection Biascited by 166
- 2019Causal Discovery from Heterogeneous/Nonstationary Data with Independent Changescited by 166
- 2016Foundations of Structural Causal Models with Cycles and Latent Variablescited by 134
- 2022Greedy Relaxations of the Sparsest Permutation Algorithmcited by 64
- 2018Structural Agnostic Modeling: Adversarial Learning of Causal Graphscited by 48
Built on by
Not cited yet.