We consider the setting where a certain response variable should be estimated from a set of descriptors, of which a subset is referred to as "meteorological" variables. The descriptors are hence independent variables such as temperature (temp), relative humidity (rh), pressure (press), wind speed (ws), etc. and are used as predictive variables when estimating the response variable, such aspollutant concentration PM 10 . Assuming a probabilistic setting, the descriptors and the response variable are random variables (RVs). Specifically, if X denotes a vector of real-valued meteorological RVs (e.g. temp, rh, press, ws), T denotes real-valued descriptor RVs that are not meteorological (e.g. Julian day), and Y denotes a real-valued response variable that should be estimated (such as PM 10 ), then the behavior of (X, T, Y ) is determined by the joint distribution p X,T,Y . We do not make any assumptions on the causal direction of the learning problem, i.e., we do not assume that the target Y is the output of a causal mechanism to inputs (X, T ) (but see also Section 4.2). We admit, however, that the causal setting, i.e.,