We focus on data-driven causal inference. In particular, we propose a new principle for causal inference based on algorithmic information theory, i.e. Kolmogorov complexity. In a nutshell, we determine how much information one data object gives about the other, and vice versa, and identify the most likely causal direction by the strongest direction of information. To apply this principle in practice, we propose ERGO, an efficient instantiation for inferring the causal direction between multivariate real-valued data pairs. ERGO is based on cumulative and Shannon entropy. Therewith, we do not have to assume distributions, nor have to restrict the type of correlation. Extensive empirical evaluation on synthetic, benchmark, and real-world data shows that ERGO is robust against both noise and dimensionality, efficient, and outperforms the state of the art by a wide margin.
Vreeken REF proposed Ergo, a causal inference framework based on relative conditional complexities,
Causal Inference by Direction of Information
SDM
Computer Science