Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Algoritmos de descubrimiento causal (PC, FCI, LiNGAM)× | DBSCAN× | Red de Atención Gráfica× | |
|---|---|---|---|
| Campo≠ | Inferencia causal | Aprendizaje automático | Aprendizaje profundo |
| Familia≠ | Regression model | Machine learning | Machine learning |
| Año de origen≠ | 2000 | 1996 | 2018 |
| Autor original≠ | Spirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM) | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. | Veličković, P. et al. |
| Tipo≠ | Causal structure learning | Density-based clustering algorithm | Graph neural network (attention-based) |
| Fuente seminal≠ | Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, Prediction, and Search (2nd ed.). MIT Press. ISBN: 978-0262194402 | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proceedings of the 2nd KDD, 226–231. link ↗ | Veličković, P. et al. (2018). Graph Attention Networks. ICLR. link ↗ |
| Alias≠ | PC algorithm, FCI algorithm, LiNGAM, causal structure learning | DBSCAN Kümeleme, density-based clustering, density-based spatial clustering | Graf Dikkat Ağı (GAT), GAT, graph attention network, attention-based graph neural network |
| Relacionados≠ | 5 | 3 | 4 |
| Resumen≠ | Causal discovery is a family of algorithms that automatically learn a directed acyclic graph (DAG) describing causal structure directly from observational data. The constraint-based PC and FCI algorithms were developed by Spirtes, Glymour and Scheines (2000), while the LiNGAM model of Shimizu et al. (2006) exploits linear non-Gaussian structure to orient edges. | DBSCAN is a density-based clustering algorithm, introduced by Ester, Kriegel, Sander and Xu in 1996, that groups together points lying in dense regions and flags points in sparse regions as noise. It is effective on noisy data and on clusters of irregular, non-spherical shapes. | The Graph Attention Network (GAT), introduced by Veličković and colleagues in 2018, is a graph neural network variant that learns how much importance to assign to each neighbouring node through a self-attention mechanism. On heterogeneous neighbourhoods and relational classification it produces results superior to graph convolutional networks (GCN). |
| ScholarGateConjunto de datos ↗ |
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