Abstract. We study the problem of identifying the cause and the effect between two univariate continuous variables \(X\) and \(Y\). The examined data is purely observational, hence it is required to make assumptions about the underlying model. Often, the independence of the noise from the cause is assumed, which is not always the case for real world data. In view of this, we present a new method, which explicitly models heteroscedastic noise. With our Heci algorithm, we can find the optimal model regularized, by an information theoretic score. In thorough experiments we show, that our ability to model heteroscedastic noise translates into a superior performance on a wide range of synthetic and real-world datasets.