TY - JOUR
T1 - Rate-agnostic (causal) structure learning
AU - Plis, Sergey
AU - Danks, David
AU - Freeman, Cynthia
AU - Calhoun, Vince
N1 - Funding Information:
SP & DD contributed equally. This work was supported by awards NIH R01EB005846 (SP); NSF IIS-1318759 (SP); NSF IIS-1318815 (DD); & NIH U54HG008540 (DD) (from the National Human Genome Research Institute through funds provided by the trans-NIH Big Data to Knowledge (BD2K) initiative). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
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M3 - Conference article
AN - SCOPUS:84965168584
SN - 1049-5258
VL - 2015-January
SP - 3303
EP - 3311
JO - Advances in Neural Information Processing Systems
JF - Advances in Neural Information Processing Systems
T2 - 29th Annual Conference on Neural Information Processing Systems, NIPS 2015
Y2 - 7 December 2015 through 12 December 2015
ER -