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Rate-Agnostic (Causal) Structure Learning
Sergey Plis · David Danks · Cynthia Freeman · Vince Calhoun

Mon Dec 07 04:00 PM -- 08:59 PM (PST) @ 210 C #57

Causal structure learning from time series data is a major scientific challenge. Existing algorithms assume that measurements occur sufficiently quickly; more precisely, they assume that the system and measurement timescales are approximately equal. In many scientific domains, however, measurements occur at a significantly slower rate than the underlying system changes. Moreover, the size of the mismatch between timescales is often unknown. This paper provides three distinct causal structure learning algorithms, all of which discover all dynamic graphs that could explain the observed measurement data as arising from undersampling at some rate. That is, these algorithms all learn causal structure without assuming any particular relation between the measurement and system timescales; they are thus "rate-agnostic." We apply these algorithms to data from simulations. The results provide insight into the challenge of undersampling.

Author Information

Sergey Plis (The Mind Research Network)
David Danks (Carnegie Mellon University)
Cynthia Freeman (The Mind Research Network)
Vince Calhoun (MRN)

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