Regression-based features represent the majority of features in causality detection algorithms that use predefined features. They come in various forms, such as the errors of polynomial regressions of various degrees, and the independence of the regression residuals from the putative cause variable. Additionally, features of conditional distribution variability, such as the standard deviation of the conditional distributions (CDS) introduced by Fonollosa, are utilized to capture distribution asymmetry.