A primary limitation is due to the examples used to train the classifiers. As widely known, the accuracy of trained classifiers depends on the quality of the training examples. In quite a few causal examples however, the joint distributions present typical features giving away the causality label, a phenomenon referred to as data leakage .

Another issue would be the presence of biases in the training set of the classifiers. For example, if the causal pairs with one categorical variable and one numerical variable are always labelled such as categorical → numerical, the learning algorithms might learn biased features on distributions due to the training set. Such biases hinder the generality of the causality classifiers, as they might be exploited by learning algorithms and induce the biased hypotheses.