Measurement Dependence Inducing Latent Causal Models
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Abstract (arXiv)
We consider the task of causal structure learning over measurement dependence inducing latent (MeDIL) causal models. We show that this task can be framed in terms of the graph theoretic problem of finding edge clique covers,resulting in an algorithm for returning minimal MeDIL causal models (minMCMs). This algorithm is non-parametric, requiring no assumptions about linearity or Gaussianity. Furthermore, despite rather weak assumptions aboutthe class of MeDIL causal models, we show that minimality in minMCMs implies some rather specific and interesting properties. By establishing MeDIL causal models as a semantics for edge clique covers, we also provide a starting point for future work further connecting causal structure learning to developments in graph theory and network science.
Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Oct 2019.
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Citing papers per year: 2021: 2; 2022: 2; 2023: 2; 2024: 0; 2025: 4; 2026: 4.
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- 2022Identifiability of deep generative models without auxiliary informationcited by 70
- 2021Learning latent causal graphs via mixture oraclescited by 55
- 2023Learning nonparametric latent causal graphs with unknown interventionscited by 34
- 2025Towards Interpretable Deep Generative Models via Causal Representation Learningcited by 12
- 2021A Distance Covariance-based Kernel for Nonlinear Causal Clustering in Heterogeneous Populationscited by 10
- 2022Learning Latent Structural Causal Modelscited by 5
- 2023Neuro-Causal Factor Analysiscited by 4
- 2026Distributional Equivalence in Linear Non-Gaussian Latent-Variable Cyclic Causal Models: Characterization and Learningcited by 4
- 2026Coarsening Causal DAG Modelscited by 2
- 2025Intervening to Learn and Compose Causally Disentangled Representationscited by 1
- 2025Causal Learning for Heterogeneous Subgroups Based on Nonlinear Causal Kernel Clusteringcited by 0
- 2025On Theoretical Identifiability of Discrete Latent Causal Graphical Modelscited by 0
- 2026Identification of Nonlinear and Dependent Latent Factor Structure through Clique Search26 Sep 2026
- 2026Hessian Rank Constraint for Learning Structure of Nonlinear Latent Variable Models21 Sep 2026
- 2026Distributional Equivalence in Linear Non-Gaussian Latent-Variable Cyclic Causal Models: Characterization and Learning5 Mar 2026
- 2026Coarsening Causal DAG Models15 Jan 2026
- 2025Intervening to Learn and Compose Causally Disentangled Representations7 Jul 2025
- 2025On Theoretical Identifiability of Discrete Latent Causal Graphical Models23 May 2025
- 2025Towards Interpretable Deep Generative Models via Causal Representation Learning15 Apr 2025
- 2025Causal Learning for Heterogeneous Subgroups Based on Nonlinear Causal Kernel Clustering20 Jan 2025
- 2023Learning nonparametric latent causal graphs with unknown interventions5 Jun 2023
- 2023Neuro-Causal Factor Analysis31 May 2023
- 2022Learning Latent Structural Causal Models24 Oct 2022
- 2022Identifiability of deep generative models without auxiliary information20 Jun 2022
- 2022Identifiability of deep generative models without auxiliary informationcited by 70
- 2021Learning latent causal graphs via mixture oraclescited by 55
- 2023Learning nonparametric latent causal graphs with unknown interventionscited by 34
- 2025Towards Interpretable Deep Generative Models via Causal Representation Learningcited by 12
- 2021A Distance Covariance-based Kernel for Nonlinear Causal Clustering in Heterogeneous Populationscited by 10
- 2022Learning Latent Structural Causal Modelscited by 5
- 2023Neuro-Causal Factor Analysiscited by 4
- 2026Distributional Equivalence in Linear Non-Gaussian Latent-Variable Cyclic Causal Models: Characterization and Learningcited by 4
- 2026Coarsening Causal DAG Modelscited by 2
- 2025Intervening to Learn and Compose Causally Disentangled Representationscited by 1
- 2025Causal Learning for Heterogeneous Subgroups Based on Nonlinear Causal Kernel Clusteringcited by 0
- 2025On Theoretical Identifiability of Discrete Latent Causal Graphical Modelscited by 0
12 most cited of 14 All 14 (JSON)
- 2026Identification of Nonlinear and Dependent Latent Factor Structure through Clique Search26 Sep 2026
- 2026Hessian Rank Constraint for Learning Structure of Nonlinear Latent Variable Models21 Sep 2026
- 2026Distributional Equivalence in Linear Non-Gaussian Latent-Variable Cyclic Causal Models: Characterization and Learning5 Mar 2026
- 2026Coarsening Causal DAG Models15 Jan 2026
- 2025Intervening to Learn and Compose Causally Disentangled Representations7 Jul 2025
- 2025On Theoretical Identifiability of Discrete Latent Causal Graphical Models23 May 2025
- 2025Towards Interpretable Deep Generative Models via Causal Representation Learning15 Apr 2025
- 2025Causal Learning for Heterogeneous Subgroups Based on Nonlinear Causal Kernel Clustering20 Jan 2025
- 2023Learning nonparametric latent causal graphs with unknown interventions5 Jun 2023
- 2023Neuro-Causal Factor Analysis31 May 2023
- 2022Learning Latent Structural Causal Models24 Oct 2022
- 2022Identifiability of deep generative models without auxiliary information20 Jun 2022
12 newest of 14 All 14 (JSON)