Learning Causal Structure from Undersampled Time Series

By David Danks, Sergey M. Plis. 2013

61 citations recorded by the source corpus.

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Citations in the corpus

Causal Discovery from Temporally Aggregated Time Series (2017, methodology): “Recently, a set of methods have been proposed to estimate the causal structure at the causal frequency from subsampled data without resorting to disaggregation techniques (Hyttinen et al., 2016; Gong et al., 2015; Plis et al., 2015a; Danks & Plis, 2013).”
Causal Discovery from Subsampled Time Series Data by Constraint Optimization (2016, background): “Danks and Plis (2013) demonstrated that, in the infinite sample limit, the causal structure G1 at the system timescale is in general underdetermined, even when the subsampling rate u is known and 1.”
Analysis of sampling artifacts on the Granger causality analysis for topology extraction of neuronal dynamics (2014, unknown): “In its application to the analysis of fMRI data, one is confronted with the unavoidable issue of low-pass ﬁltering and down-sampling in processing the BOLD signal (Roebroeck et al., 2005; Danks and Plis, 2013).”
Rate-Agnostic (Causal) Structure Learning (2015, methodology): “For given G(1) and u, there is an efficient algorithm [9] for calculating G, but it is only useful in learning if we have stopping rules that constrain which G(1) and u should ever be considered.”
Discovering Temporal Causal Relations from Subsampled Data (2015, background): “The work by Danks & Plis (2014) aims to infer the causal structure at the correct causal frequency directly from the causal structure learned from the subsampled data; they do not assume any specific form for the causal relations and their method is completely nonparametric, but on the other hand,…”
A Survey of Learning Causality with Data: Problems and Methods (2018, background): “The key assumption for the recently proposed methods [47, 63, 110] is that there exists a true timescale or causal frequency at which we can discover proper causal graph structures with the highest confidence [33].”
A Survey of Learning Causality with Data (2018, background): “ssumption for the recently proposed methods [47, 63, 110] is that there exists a true timescale or causal frequency at which we can discover proper causal graph structures with the highest confidence [33]. Furthermore, for learning causal relationship in time series data, hidden time series acts lik”
Review of Causal Discovery Methods Based on Graphical Models (2019, methodology): “It is well-established that the most common procedure, Granger causality, is very sensitive to temporal aggregation or subsampling [for the eﬀect of aggregation and subsampling and some possible ways to deal with them, see (Danks and Plis, 2013; Gong* et al., 2015; Gong et al., 2017)].”
Identifiability and estimation of structural vector autoregressive models for subsampled and mixed-frequency time series. (2017, background): “Recently, the problem of causal discovery in subsampled time series has been studied drawing from methods in causal structure learning using graphical models [7, 8, 9, 10].”
Causal Learning for Partially Observed Stochastic Dynamical Systems (2018, background): “Danks and Plis (2013) make a similar point in the case of subsampled time series.”
Mesochronal Structure Learning (2015, methodology): “Undersampling was explicitly addressed in [2], but they focused on the “forward” problem of un-dersampling: given a structure at τ S , what structure will be realized at τ M ?”
Markov equivalence of marginalized local independence graphs (2018, background): “Related research has been concerned with inferring the graph structure from subsampled time series, but under the assumption of no latent processes; see, for example, [9, 22].”
Granger Causality: A Review and Recent Advances (2021, background): “Recently, causal discovery in subsampled time series has been studied with methods in causal structure learning using graphical models (Danks and Plis, 2013; Plis et al., 2015; Hyttinen et al., 2016).”