Reviews: Specific and Shared Causal Relation Modeling and Mechanism-Based Clustering

In many scenarios, the causal relationships considered over a set of variables vary across groups and at the same time share some common causal relationships. So it is better to find different causal graphs for each individual. This paper solves this problem by first dividing the set of agents into a number of groups and then finding a causal graph for each group. The authors propose a model over m variables that includes both instantaneous effects and time-lagged effects. Ideally, we would have to estimate this model separately for each user, but that might be impossible with a small number of samples.