mlsanityData to 7 Oct 2026

Identifiability of AMP chain graph models

arXiv 2106.09350 ↗First posted 17 Jun 2021cs.DS stat.MLcited by 0

Not on the map. Why?

Abstract (arXiv)

We study identifiability of Andersson-Madigan-Perlman (AMP) chain graph models, which are a common generalization of linear structural equation models and Gaussian graphical models. AMP models are described by DAGs on chain components which themselves are undirected graphs. For a known chain component decomposition, we show that the DAG on the chain components is identifiable if the determinants of the residual covariance matrices of the chain components are monotone non-decreasing in topological order. This condition extends the equal variance identifiability criterion for Bayes nets, and it can be generalized from determinants to any super-additive function on positive semidefinite matrices. When the component decomposition is unknown, we describe conditions that allow recovery of the full structure using a polynomial time algorithm based on submodular function minimization. We also conduct experiments comparing our algorithm's performance against existing baselines.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 22 of its 79 references that are in the map. First posted on arXiv 17 Jun 2021.

Builds on

22 of 79 references.

References · 22

  1. 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 745
  2. 2019DAG-GNN: DAG Structure Learning with Graph Neural Networkscited by 329
  3. 2013Causal Discovery with Continuous Additive Noise Modelscited by 252
  4. 2019Gradient-Based Neural DAG Learningcited by 227
  5. 2019Learning Sparse Nonparametric DAGscited by 207
  6. 2020DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networkscited by 66
  7. 2018On Causal Discovery with Equal Variance Assumptioncited by 55
  8. 2015A Faster Cutting Plane Method and its Implications for Combinatorial and Convex Optimizationcited by 50
  9. 2017Learning linear structural equation models in polynomial time and sample complexitycited by 49
  10. 2013Learning Sparse Causal Models is not NP-hardcited by 48
  11. 2015High-dimensional consistency in score-based and hybrid structure learningcited by 44
  12. 2013A Discovery Algorithm for Directed Cyclic Graphscited by 33
10 more
  1. 2014Distinguishing cause from effect using observational data: methods and benchmarkscited by 33
  2. 2020A polynomial-time algorithm for learning nonparametric causal graphscited by 29
  3. 2017Learning Identifiable Gaussian Bayesian Networks in Polynomial Time and Sample Complexitycited by 27
  4. 2020Causal Discovery from Incomplete Data: A Deep Learning Approachcited by 26
  5. 2017Geometric Rescaling Algorithms for Submodular Function Minimizationcited by 2
  6. 2020AMP Chain Graphs: Minimal Separators and Structure Learning Algorithmscited by 2
  7. 2013Marginal AMP Chain Graphscited by 1
  8. 2013Every LWF and AMP chain graph originates from a set of causal modelscited by 1
  9. 2015Alternative Markov and Causal Properties for Acyclic Directed Mixed Graphscited by 1
  10. 2017Identification of Strong Edges in AMP Chain Graphscited by 1

Built on by

Not cited yet.