Towards Efficient Local Causal Structure Learning
This work addresses efficiency bottlenecks in causal discovery for researchers and practitioners, representing an incremental improvement over existing methods.
The paper tackles the problem of distinguishing direct causes from direct effects in local causal structure learning by proposing the ELCS algorithm, which uses N-structures and an efficient Markov Blanket discovery subroutine, achieving better accuracy and efficiency than state-of-the-art methods as validated on eight Bayesian networks.
Local causal structure learning aims to discover and distinguish direct causes (parents) and direct effects (children) of a variable of interest from data. While emerging successes have been made, existing methods need to search a large space to distinguish direct causes from direct effects of a target variable T. To tackle this issue, we propose a novel Efficient Local Causal Structure learning algorithm, named ELCS. Specifically, we first propose the concept of N-structures, then design an efficient Markov Blanket (MB) discovery subroutine to integrate MB learning with N-structures to learn the MB of T and simultaneously distinguish direct causes from direct effects of T. With the proposed MB subroutine, ELCS starts from the target variable, sequentially finds MBs of variables connected to the target variable and simultaneously constructs local causal structures over MBs until the direct causes and direct effects of the target variable have been distinguished. Using eight Bayesian networks the extensive experiments have validated that ELCS achieves better accuracy and efficiency than the state-of-the-art algorithms.