https://github.com/huawei-noah/trustworthyAI/tree/master/gcastle > gCastle is a causal structure learning toolchain developed by Huawei Noah's Ark Lab. The package contains various functionality related to causal learning and evaluation, including: - [[PCアルゴリズム|PC]] - [[ANN]] - [[DirectLiNGAM]] - ICALiNGAM - [[Greedy Equivalence Search|GES]] - PNL ## アルゴリズム | Algorithm | Category | Description | Status | | :--: | :-- | :-- | :--: | | [[PCアルゴリズム|PC]] | IID/Constraint-based | A classic causal discovery algorithm based on conditional independence tests | v1.0.3 | | [ANM](https://webdav.tuebingen.mpg.de/causality/NIPS2008-Hoyer.pdf) | IID/Function-based | Nonlinear causal discovery with additive noise models | v1.0.3 | | [[DirectLiNGAM]] | IID/Function-based | A direct learning algorithm for linear non-Gaussian acyclic model (LiNGAM) | v1.0.3 | | [ICALiNGAM](https://dl.acm.org/doi/10.5555/1248547.1248619) | IID/Function-based | An ICA-based learning algorithm for linear non-Gaussian acyclic model (LiNGAM) | v1.0.3 | | [GES](https://www.jmlr.org/papers/volume3/chickering02b/chickering02b.pdf) | IID/Score-based | A classical Greedy Equivalence Search algorithm | v1.0.3 | | [PNL](https://arxiv.org/abs/1205.2599) | IID/Funtion-based | Causal discovery based on the post-nonlinear causal assumption | v1.0.3 | | [[NOTEARS]] | IID/Gradient-based | A gradient-based algorithm for linear data models (typically with least-squares loss) | v1.0.3 | | [NOTEARS-MLP](https://arxiv.org/abs/1909.13189) | IID/Gradient-based | A gradient-based algorithm using neural network modeling for non-linear causal relationships | v1.0.3 | | [NOTEARS-SOB](https://arxiv.org/abs/1909.13189) | IID/Gradient-based | A gradient-based algorithm using Sobolev space modeling for non-linear causal relationships | v1.0.3 | | [NOTEARS-lOW-RANK](https://arxiv.org/abs/2006.05691) | IID/Gradient-based | Adapting NOTEARS for large problems with low-rank causal graphs | v1.0.3 | | [[2019__ICML__DAG-GNN - DAG Structure Learning with Graph Neural Networks|DAG-GNN]] | IID/Gradient-based | DAG Structure Learning with Graph Neural Networks | v1.0.3 | | [GOLEM](https://arxiv.org/abs/2006.10201) | IID/Gradient-based | A more efficient version of NOTEARS that can reduce number of optimization iterations | v1.0.3 | | [GraNDAG](https://arxiv.org/abs/1906.02226) | IID/Gradient-based | A gradient-based algorithm using neural network modeling for non-linear additive noise data | v1.0.3 | | [MCSL](https://arxiv.org/abs/1910.08527) | IID/Gradient-based | A gradient-based algorithm for non-linear additive noise data by learning the binary adjacency matrix| v1.0.3 | | [GAE](https://arxiv.org/abs/1911.07420) | IID/Gradient-based | A gradient-based algorithm using graph autoencoder to model non-linear causal relationships| v1.0.3 | | [RL](https://arxiv.org/abs/1906.04477) | IID/Gradient-based | A RL-based algorithm that can work with flexible score functions (including non-smooth ones) | v1.0.3 | | [CORL](https://arxiv.org/abs/2105.06631) | IID/Gradient-based | A RL- and order-based algorithm that improves the efficiency and scalability of previous RL-based approach | v1.0.3 | | [TTPM](https://arxiv.org/abs/2105.10884) | EventSequence/Function-based | A causal structure learning algorithm based on Topological Hawkes process for spatio-temporal event sequences |v1.0.3 | | [HPCI](https://arxiv.org/abs/2105.03092) | EventSequence/Hybrid | A causal structure learning algorithm based on Hawkes process and CI tests for event sequences | under development. |