Gradient-Based Neural DAG Learning
Abstract
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.
Cite
@article{arxiv.1906.02226,
title = {Gradient-Based Neural DAG Learning},
author = {Sébastien Lachapelle and Philippe Brouillard and Tristan Deleu and Simon Lacoste-Julien},
journal= {arXiv preprint arXiv:1906.02226},
year = {2020}
}
Comments
Appears in: Proceedings of the Eighth International Conference on Learning Representations (ICLR 2020). 23 pages