The discovery of non-linear causal relationship under additive non-Gaussian
noise models has attracted considerable attention recently because of their
high flexibility. In this paper, we propose a novel causal inference algorithm
called least-squares independence regression (LSIR). LSIR learns the additive
noise model through the minimization of an estimator of the squared-loss mutual
information between inputs and residuals.