Abstract: Certain aspects of the present disclosure relate to methods and apparatus for inferring causal relationship between random processes using a temporal learning algorithm. The temporal learning algorithm determines structure of a causal graph with a set of nodes. Input to the nodes may be binary time series (e.g., random processes). The output of the temporal learning algorithm may be a labeled directed graph in which the direction of the connection between each two node indicates causal direction and the strength of connectivity between the nodes indicates intensity of the causal influence. The temporal learning algorithm may iteratively update strength of connections between nodes to track variations in real time.