Causal Effect Identification in LiNGAM Models with Latent Confounders
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866914822038224896 |
|---|---|
| author | Tramontano, Daniele Kivva, Yaroslav Salehkaleybar, Saber Drton, Mathias Kiyavash, Negar |
| author_facet | Tramontano, Daniele Kivva, Yaroslav Salehkaleybar, Saber Drton, Mathias Kiyavash, Negar |
| contents | We study the generic identifiability of causal effects in linear non-Gaussian acyclic models (LiNGAM) with latent variables. We consider the problem in two main settings: When the causal graph is known a priori, and when it is unknown. In both settings, we provide a complete graphical characterization of the identifiable direct or total causal effects among observed variables. Moreover, we propose efficient algorithms to certify the graphical conditions. Finally, we propose an adaptation of the reconstruction independent component analysis (RICA) algorithm that estimates the causal effects from the observational data given the causal graph. Experimental results show the effectiveness of the proposed method in estimating the causal effects. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_02049 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Causal Effect Identification in LiNGAM Models with Latent Confounders Tramontano, Daniele Kivva, Yaroslav Salehkaleybar, Saber Drton, Mathias Kiyavash, Negar Machine Learning Methodology We study the generic identifiability of causal effects in linear non-Gaussian acyclic models (LiNGAM) with latent variables. We consider the problem in two main settings: When the causal graph is known a priori, and when it is unknown. In both settings, we provide a complete graphical characterization of the identifiable direct or total causal effects among observed variables. Moreover, we propose efficient algorithms to certify the graphical conditions. Finally, we propose an adaptation of the reconstruction independent component analysis (RICA) algorithm that estimates the causal effects from the observational data given the causal graph. Experimental results show the effectiveness of the proposed method in estimating the causal effects. |
| title | Causal Effect Identification in LiNGAM Models with Latent Confounders |
| topic | Machine Learning Methodology |
| url | https://arxiv.org/abs/2406.02049 |