2015/04/22 by Kun Zhang, Jiji Zhang, Zhang, Kun +3
Computer Science · Mathematics · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Methodology (stat.ME) #cs.AI #stat.ME
paper · pdf · doi:10.48550/arxiv.1504.05651
11 pages, 4 figures, published in Proceedings of the 15th conference on Theoretical Aspects of Rationality and Knowledge (TARK'15)
arxiv created 2015/04/22 · arxiv updated 2015/04/23
Recent developments in structural equation modeling have produced several methods that can usually distinguish cause from effect in the two-variable case. For that purpose, however, one has to impose substantial structural constraints or smoothness assumptions on the functional causal models. In this paper, we consider the problem of determining the causal direction from a related but different point of view, and propose a new framework for causal direction determination. We show that it is possible to perform causal inference based on the condition that the cause is "exogenous" for the parameters involved in the generating process from the cause to the effect. In this way, we avoid the structural constraints required by the SEM-based approaches. In particular, we exploit nonparametric methods to estimate marginal and conditional distributions, and propose a bootstrap-based approach to test for the exogeneity condition; the testing results indicate the causal direction between two variables. The proposed method is validated on both synthetic and real data.