A Graph Autoencoder Approach to Causal Structure Learning
Abstract
Causal structure learning has been a challenging task in the past decades and several mainstream approaches such as constraint- and score-based methods have been studied with theoretical guarantees. Recently, a new approach has transformed the combinatorial structure learning problem into a continuous one and then solved it using gradient-based optimization methods. Following the recent state-of-the-arts, we propose a new gradient-based method to learn causal structures from observational data. The proposed method generalizes the recent gradient-based methods to a graph autoencoder framework that allows nonlinear structural equation models and is easily applicable to vector-valued variables. We demonstrate that on synthetic datasets, our proposed method outperforms other gradient-based methods significantly, especially on large causal graphs. We further investigate the scalability and efficiency of our method, and observe a near linear training time when scaling up the graph size.
Cite
@article{arxiv.1911.07420,
title = {A Graph Autoencoder Approach to Causal Structure Learning},
author = {Ignavier Ng and Shengyu Zhu and Zhitang Chen and Zhuangyan Fang},
journal= {arXiv preprint arXiv:1911.07420},
year = {2019}
}
Comments
NeurIPS 2019 Workshop "Do the right thing": machine learning and causal inference for improved decision making