Learning Why Things Change: The Difference-Based Causality Learner
  In this paper, we present the Difference- Based Causality Learner (DBCL), an
algorithm for learning a class of discrete-time dynamic models that represents
all causation across time by means of difference equations driving change in a
system. We motivate this representation with real-world mechanical systems and
prove DBCL's correctness for learning structure from time series data, an
endeavour that is complicated by the existence of latent derivatives that have
to be detected. We also prove that, under common assumptions for causal
discovery, DBCL will identify the presence or absence of feedback loops, making
the model more useful for predicting the effects of manipulating variables when
the system is in equilibrium. We argue analytically and show empirically the
advantages of DBCL over vector autoregression (VAR) and Granger causality
models as well as modified forms of Bayesian and constraintbased structure
discovery algorithms. Finally, we show that our algorithm can discover causal
directions of alpha rhythms in human brains from EEG data.
