1. A method, implemented on a computer, for causal modeling, comprising: modeling, using the computer, a data set, said modeling comprising estimating a reverse Bayesian forest for the data set, said reverse Bayesian forest comprising a set of tree structures, wherein in each of said tree structures a direction of edges leads from a root of the tree structures towards leaves of the tree structures; detecting outliers in a separate data set, said detecting comprising: applying the reverse Bayesian forest to the separate data set to obtain a probability value assigned to data points in the separate data set; and identifying outliers in the separate data set by evaluating the probability value given by the reverse Bayesian forest; and determining the root cause of the outliers by searching for the outliers in the reverse Bayesian forest and determining root cause feature dependencies for the outliers.