Rate-Agnostic (Causal) Structure Learning

By Sergey M. Plis, David Danks, Cynthia Freeman, Vince D. Calhoun. 2015

28 citations recorded by the source corpus.

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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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).”
DYNOTEARS: Structure Learning from Time-Series Data (2020, unknown): “See for example Gong et al. (2015); Hyttinen et al. (2016); Plis et al. (2015); Cook et al. (2017).”
A Survey of Learning Causality with Data (2018, background): “hat only a low-resolution version of the original time series is available, for example, we only observe the time series every k time steps [131]. 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 pro”
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).”
Causal Discovery from Subsampled Time Series Data by Constraint Optimization (2016, methodology): “Recently, the first exact search algorithm for finding the G1s that are consistent with a given H for a known u was presented by Plis et al. (2015b); it represents the current state-of-the-art.”
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].”
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].”