Using observed sequence to orient causal networks

In learning causal networks, typically cross-sectional data are used and the sequence among the network nodes is learned through conditional independence. Sequence is inherently a longitudinal concept. We propose to learn sequence of events in longitudinal data and use it to orient arc directions in a network learned from cross-sectional data. The network is learned from cross-sectional data using various established algorithms, with one …