A Compression-Based Dependence Measure for Causal Discovery by Additive Noise Models

In this work, we introduce a novel compression-based dependence measure (CDM) for causal discovery. Our proposed measure leverages data compression to quantify dependence, offering a new approach that is effective even with small data sizes.

Through extensive simulations with general additive noise models, causal additive models, and linear non-Gaussian acyclic models, we demonstrate the relative superiority of CDM over existing methods. Additionally, we validate our approach using the cause-effect pairs benchmark dataset, where CDM shows comparable accuracy across various sample sizes.

To close, we discuss the sensitivity of CDM to data scales—an issue shared by many causal discovery methods. Despite this, CDM presents a promising way to take advantage of data compression for causal discovery.

Recommended citation: Ong, Hans Jarett J., Lim, Brian Godwin S., Tiu, Benedict Ryan C., Tan, Renzo Roel P., and Ikeda, Kazushi. (2025). "A Compression-Based Dependence Measure for Causal Discovery by Additive Noise Models." International Conference on Neural Information Processing, pp. 61–75. Springer, Singapore.