  In recent years, a lot of research has been conducted within the area of
causal inference and causal learning. Many methods have been developed to
identify the cause-effect pairs in models and have been successfully applied to
observational real-world data to determine the direction of causal
relationships. Yet in bivariate situations, causal discovery problems remain
challenging. One class of such methods, that also allows tackling the bivariate
case, is based on Additive Noise Models (ANMs). Unfortunately, one aspect of
these methods has not received much attention until now: what is the impact of
different noise levels on the ability of these methods to identify the
direction of the causal relationship. This work aims to bridge this gap with
the help of an empirical study. We test Regression with Subsequent Independence
Test (RESIT) using an exhaustive range of models where the level of additive
noise gradually changes from 1\% to 10000\% of the causes' noise level (the
latter remains fixed). Additionally, the experiments in this work consider
several different types of distributions as well as linear and non-linear
models. The results of the experiments show that ANMs methods can fail to
capture the true causal direction for some levels of noise.
