A Graph Autoencoder Approach to Causal Structure Learning
This work addresses causal structure learning for researchers and practitioners in machine learning, offering an incremental improvement by extending existing gradient-based methods to a more flexible framework.
The authors tackled the problem of causal structure learning by proposing a graph autoencoder framework that generalizes gradient-based methods to handle nonlinear structural equation models and vector-valued variables, demonstrating significant outperformance over other gradient-based methods on synthetic datasets, especially for large graphs, with near linear training time scaling.
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.