Causal Learner is a toolbox for learning causal structure and Markov blanket (MB) from data. It integrates functions generating simulated Bayesian network data, set of state-of-the-art global algorithms, local MB evaluating algorithms. The data generation part written in R, the rest MATLAB. aims to provide researchers practitioners with an open-source platform development evaluation new project...

We consider two variables that are related to each other by an invertible function. While it has previously been shown that the dependence structure of the noise can provide hints to determine which of the two variables is the cause, we presently show that even in the deterministic (noise-free) case, there are asymmetries that can be exploited for causal inference. Our method is based on the id...

System dynamics (SD) is a well-known methodology for analyzing the structure and the behavior of business systems and the environment. The utility of SD has been proven in numerous cases. However, decision makers have reservations using SD to support their decision processes. The reasons can be a) decision makers have problems to design a causal model under given time constraints (aspect of met...

This paper proposes a novel problem setting of selectional preference (SP) between a predicate and its arguments, called as context-sensitive SP (CSP). CSP models the narrative consistency between the predicate and preceding contexts of its arguments, in addition to the conventional SP based on semantic types. Furthermore, we present a novel CSP model that extends the neural SP model (Van de Cr...