6,033 research outputs found
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
Graphical models for marked point processes based on local independence
A new class of graphical models capturing the dependence structure of events
that occur in time is proposed. The graphs represent so-called local
independences, meaning that the intensities of certain types of events are
independent of some (but not necessarily all) events in the past. This dynamic
concept of independence is asymmetric, similar to Granger non-causality, so
that the corresponding local independence graphs differ considerably from
classical graphical models. Hence a new notion of graph separation, called
delta-separation, is introduced and implications for the underlying model as
well as for likelihood inference are explored. Benefits regarding facilitation
of reasoning about and understanding of dynamic dependencies as well as
computational simplifications are discussed.Comment: To appear in the Journal of the Royal Statistical Society Series
- …