Gradient-Based Neural DAG Learning

By Sébastien Lachapelle, Philippe Brouillard, Tristan Deleu, Simon Lacoste-Julien. 2019

359 citations recorded by the source corpus.

We propose a novel score-based approach to learning a directed acyclic graph (DAG) from observational data. We adapt a recently proposed continuous constrained optimization formulation to allow for nonlinear relationships between variables using neural networks. This extension allows to model complex interactions while avoiding the combinatorial nature of the problem. In addition to comparing our method to existing continuous optimization methods, we provide missing empirical comparisons to nonlinear greedy search methods. On both synthetic and real-world data sets, this new method outperforms current continuous methods on most tasks, while being competitive with existing greedy search methods on important metrics for causal inference.

Subfields: Artificial Intelligence, Causal Inference & Discovery

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Citations in the corpus

Differentiable Causal Discovery from Interventional Data (2020, methodology): “Several other methods use neural networks to model nonlinear relations via fθ and have been shown to be competitive with classical methods [27, 53].”
An Analysis of the Adaptation Speed of Causal Models (2020, methodology): “, 2000) or score-based methods using greedy search (Chickering, 2002) or more recently continuous optimization (Zheng et al., 2018; Lachapelle et al., 2020).”
A theory of independent mechanisms for extrapolation in generative models (2020, background): “Deep neural network have been leveraged in causal inference for learning causal graphs between observed variables (Lopez-Paz and Oquab 2016) and associated causal effects (Louizos et al. 2017; Shalit, Johansson, and Sontag 2017; Kocaoglu et al. 2017; Lachapelle et al. 2019; Zhu, Ng, and Chen 2019).”
Masked Gradient-Based Causal Structure Learning (2019, methodology): “Subsequent works such as DAG-GNN [37] and GraN-DAG [17] have extended it to handle nonlinear cases.”
Differentiable Causal Discovery Under Latent Interventions (2022, methodology): “Several other methods apply non-linear models such as neural networks (Lachapelle et al., 2019; Zheng et al., 2020) and deﬁne W G differently.”
Learning Sparse Nonparametric DAGs (2019, methodology): “This also distinguishes this paper from concurrent work by Lachapelle et al. (2019) that focuses on neural network-based nonlinearities in the local conditional probabilities.”
Discrete Graph Structure Learning for Forecasting Multiple Time Series (2021, methodology): “Gradient-based approaches in this context include NOTEARS (Zheng et al., 2018), DAG-GNN (Yu et al., 2019), and GraN-DAG (Lachapelle et al., 2020).”
Causal Induction from Visual Observations for Goal Directed Tasks (2019, neutral): “In addition, adversarial learning (Kalainathan et al., 2018), graph neural networks (Yu et al., 2019), and gradient-based DAG learning (Lachapelle et al., 2019) have been recently introduced to causal discovery, but largely focusing on small synthetic datasets.”
Variational Causal Networks: Approximate Bayesian Inference over Causal Structures (2021, methodology): “[24] extend the linear model of [38] to non-linear case using neural networks.”