Differentiable Causal Discovery of Linear Non-Gaussian Acyclic Models Under Unmeasured Confounding
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866916577583038464 |
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| author | Morinishi, Yoshimitsu Shimizu, Shohei |
| author_facet | Morinishi, Yoshimitsu Shimizu, Shohei |
| contents | We propose a novel score-based causal discovery method, named ABIC LiNGAM, which extends the linear non-Gaussian acyclic model (LiNGAM) framework to address the challenges of causal structure estimation in scenarios involving unmeasured confounders. By introducing the assumption that error terms follow a multivariate generalized normal distribution, our method leverages continuous optimization techniques to recover acyclic directed mixed graphs (ADMGs), including causal directions rather than just equivalence classes. We provide theoretical guarantees on the identifiability of causal parameters and demonstrate the effectiveness of our approach through extensive simulations and applications to real-world datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_12854 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Differentiable Causal Discovery of Linear Non-Gaussian Acyclic Models Under Unmeasured Confounding Morinishi, Yoshimitsu Shimizu, Shohei Methodology We propose a novel score-based causal discovery method, named ABIC LiNGAM, which extends the linear non-Gaussian acyclic model (LiNGAM) framework to address the challenges of causal structure estimation in scenarios involving unmeasured confounders. By introducing the assumption that error terms follow a multivariate generalized normal distribution, our method leverages continuous optimization techniques to recover acyclic directed mixed graphs (ADMGs), including causal directions rather than just equivalence classes. We provide theoretical guarantees on the identifiability of causal parameters and demonstrate the effectiveness of our approach through extensive simulations and applications to real-world datasets. |
| title | Differentiable Causal Discovery of Linear Non-Gaussian Acyclic Models Under Unmeasured Confounding |
| topic | Methodology |
| url | https://arxiv.org/abs/2501.12854 |