Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Algoritmos de descubrimiento causal (PC, FCI, LiNGAM)× | Red de Atención Gráfica× | |
|---|---|---|
| Campo≠ | Inferencia causal | Aprendizaje profundo |
| Familia≠ | Regression model | Machine learning |
| Año de origen≠ | 2000 | 2018 |
| Autor original≠ | Spirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM) | Veličković, P. et al. |
| Tipo≠ | Causal structure learning | 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 | Veličković, P. et al. (2018). Graph Attention Networks. ICLR. link ↗ |
| Alias≠ | PC algorithm, FCI algorithm, LiNGAM, causal structure learning | Graf Dikkat Ağı (GAT), GAT, graph attention network, attention-based graph neural network |
| Relacionados≠ | 5 | 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. | 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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