Masked Gradient-Based Causal Structure Learning
By Ignavier Ng, Zhuangyan Fang, Shengyu Zhu, Zhitang Chen. 2019
143 citations recorded by the source corpus.
Learning causal graphical models based on directed acyclic graphs is an important task in causal discovery and causal inference. We consider a general framework towards efficient causal structure learning with potentially large graphs. Within this framework, we propose a masked gradient-based structure learning method based on binary adjacency matrix that exists for any structural equation model. To enable first-order optimization methods, we use Gumbel-Softmax approach to approximate the binary valued entries of the adjacency matrix, which usually results in real values that are close to zero or one. The proposed method can readily include any differentiable score function and model function for learning causal structures. Experiments on both synthetic and real-world datasets are conducted to show the effectiveness of our approach.
Subfields: Artificial Intelligence, Prompting & Reasoning
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Simon Lacoste-Julien
- Alexandre Lacoste
- Rémi Le Priol
- Pau Rodr'iguez L'opez
- Sébastien Lachapelle
- Richard Bowden
- Alexandre Drouin
- Philippe Brouillard
Citations in the corpus
- Disentanglement via Mechanism Sparsity Regularization: A New Principle for Nonlinear ICA (2021, methodology): “This strategy has been used successfully in previous work to enable gradient-based causal discovery (Ng et al., 2019; Brouillard et al., 2020).”
- Differentiable Causal Discovery from Interventional Data (2020, methodology): “In some methods, the parameter θ can be partitioned into θ1 and θ2 such that fθ = fθ1 and Aθ = Aθ2 [21, 32, 23] while in others, such a decoupling is not possible, i.”
- D’ya Like DAGs? A Survey on Structure Learning and Causal Discovery (2021, methodology): “) [138] 2020 low/medium NN yes no DAG GranDAG [139] 2020 low NN yes no DAG MaskedNN [176] 2020 low NN yes no DAG CausalVAE [267] 2020 image NN yes yes DAG CAREFL [126] 2020 low NN yes no DAG / direction Varando [251] 2020 low linear yes no DAG NO TEARS+ [280] 2020 low non-linear yes no DAG ICL [258]”