<anchor xmlns="http://www.tei-c.org/ns/1.0" xml:id="id_docs-internal-guid-d5fb32d9-7fff-63ca-921e-a71e4c5bf86b" />Obviously the variables publisher, author and series are influencing the distributions of our features, the words of the texts, and the variable, we want to predict, the genre. In a classical scientific model publisher, author and series would be called <hi xmlns="http://www.tei-c.org/ns/1.0" rend="italic">confounding variables</hi>, but in textclassification the role of confounding has been mostly overlooked,probably because usually the main goal is prediction and not causalinference (Landeiro / Culotta 2016). Confounding variables are those factors in statistical models, that lead to false correlations or bias. For example, in an experiment that investigates the relationship between age of a person and the tendency to drive fast, the car would be a confounding variable. Because older people have probably a higher income and own faster cars. Something very similar is happening here. In the next section, we will apply a standard measure to control for confounding variables (restriction), while keeping the machine learning setup.