LEARNING DETERMINISTIC CAUSAL RELATIONS

Abstract

Learning causal relations from observational data is a fundamental task in knowledge discovery. Recently, an Information Geometric Causal Inference (IGCI) framework is proposed, which represents causal relations by deterministic functions, defines causal mechanisms by a cause-mapping independence postulate and learns causal directions by an information geometry formulation. Observing IGCI's limitation in representing general causal relations and its ambiguity in inferring causal directions, we generalize IGCI's original postulate, and propose a new dependence causal inference (DCI) method where linear correlation and a new cross likelihood (CL) measure are introduced. We prove that CL dependence measure incorporates IGCI as its special case in some sense. Experimental results on synthetic and real-world data verify the effectiveness of our generalization and methods, and show that our method is more robust to disturbances on real data sets.