SELF: Structural Equational Likelihood Framework for Causal Discovery


  • Ruichu Cai Guangdong University of Technology
  • Jie Qiao Guangdong University of Technology
  • Zhenjie Zhang Advanced Digital Sciences Center, Illinois at Singapore Pte. Ltd.
  • Zhifeng Hao Guangdong University of Technology; Foshan University


causal discovery, structural equation model, likelihood function


Causal discovery without intervention is well recognized as a challenging yet powerful data analysis tool, boosting the development of other scientific areas, such as biology, astronomy, and social science. The major technical difficulty behind the observation-based causal discovery is to effectively and efficiently identify causes and effects from correlated variables given the existence of significant noises. Previous studies mostly employ two very different methodologies under Bayesian network framework, namely global likelihood maximization and locally complexity analysis over marginal distributions. While these approaches are effective in their respective problem domains, in this paper, we show that they can be combined to formulate a new global optimization model with local statistical significance, called structural equational likelihood framework (or SELF in short). We provide thorough analysis on the soundness of the model under mild conditions and present efficient heuristic-based algorithms for scalable model training. Empirical evaluations using XGBoost validate the superiority of our proposal over state-of-the-art solutions, on both synthetic and real world causal structures.




How to Cite

Cai, R., Qiao, J., Zhang, Z., & Hao, Z. (2018). SELF: Structural Equational Likelihood Framework for Causal Discovery. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from



AAAI Technical Track: Knowledge Representation and Reasoning