title: Rate-Agnostic (Causal) Structure Learning.
abstract: Causal structure learning from time series data is a major scientific challenge. Extant algorithms assume that measurements occur sufficiently quickly; more precisely, they assume approximately equal system and measurement timescales. In many domains, however, measurements occur at a significantly slower rate than the underlying system changes, but the size of the timescale mismatch is often unknown. This paper develops three causal structure learning algorithms, each of which discovers all dynamic causal graphs that explain the observed measurement data, perhaps given undersampling. That is, these algorithms all learn causal structure in a "rate-agnostic" manner: they do not assume any particular relation between the measurement and system timescales. We apply these algorithms to data from simulations to gain insight into the challenge of undersampling.
authors.affiliation: The Mind Research Network, Albuquerque, NM.
authors.forename: Sergey
authors.initials: S
authors.lastname: Plis
authors.affiliation: Carnegie-Mellon University, Pittsburgh, PA.
authors.forename: David
authors.initials: D
authors.lastname: Danks
authors.affiliation: The Mind Research Network, CS Dept., University of New Mexico, Albuquerque, NM.
authors.forename: Cynthia
authors.initials: C
authors.lastname: Freeman
authors.affiliation: The Mind Research Network, ECE Dept., University of New Mexico, Albuquerque, NM.
authors.forename: Vince
authors.initials: V
authors.lastname: Calhoun