Learning linear cyclic causal models with latent variables

By Antti Hyttinen, Frederick Eberhardt, Patrik O. Hoyer. 2012

150 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology, Causal Inference & Discovery

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Citations in the corpus

Causal Models for Dynamical Systems (2020, background): “…and on causal discovery [e.g. Spirtes et al., 2000, Pearl, 2009, Shpitser, 2018, Richardson and Spirtes, 2002, Hern´an and Robins, 2006, Evans, 2015, Richardson et al., 2017, Hyttinen et al., 2012, Verma and Pearl, 1991, Silva et al., 2006], more work is needed in the case of dynamical systems.”
Causal Structure Learning (2017, background): “Hyttinen et al. (2012) also makes use of intervention data to learn feedback models, assuming do-interventions, while Peters et al. (2016) permits to build a graph nodewise by estimating the parental set of each node separately.”
Experiment selection for causal discovery (2013, background): “In our work (Eberhardt, 2007; Eberhardt et al., 2010; Hyttinen et al., 2011, 2012a,b) we found that many of these questions concerning the optimal selection of experiments are equivalent to graph-theoretic or combinatoric problems for which, in several cases, there exist solutions in the…”
Joint Causal Inference from Multiple Contexts (2016, background): “…graph by combining these statistics, but never directly compare data from different contexts (Claassen and Heskes, 2010; Tillman and Spirtes, 2011; Hyttinen et al., 2012, 2014; Triantafillou and Tsamardinos, 2015; Rothenhäusler et al., 2015; Forré and Mooij, 2018); (b) methods that pool all…”
Constraint-based causal discovery from multiple interventions over overlapping variable sets (2014, methodology): “Causal discovery methods that allow cycles Hyttinen et al. (2013) on the other hand rely on the assumption of linearity, which is also known to be heavily violated in such networks.”
UvA-DARE ( Digital Academic Repository ) Causal Consistency of Structural Equation Models (2017, unknown): “[10, 12, 14, 15] and [13] to meaningfully interpret cyclic SEMs.”
Foundations of structural causal models with cycles and latent variables (2016, background): “However, causal cycles may arise when one approximates such systems over time [16, 39, 40] or when one describes the equilibrium states of these systems [3, 5, 24, 26, 30, 43, 53].”
Comparison of Strategies for Scalable Causal Discovery of Latent Variable Models from Mixed Data (2018, background): “Though research in graphical causalmodels has been ongoing for several years [1,9,15,21,26] most algorithms on mixed data have been developed to learn only undirected graphs (not causal).”
From Random Differential Equations to Structural Causal Models: the stochastic case (2018, background): “One of the advantages of SCMs over other causal modeling tools is that they have the ability to deal with cyclic causal relationships (Spirtes, 1995; Hyttinen et al., 2012; Mooij et al., 2011; Forré and Mooij, 2017; Bongers et al., 2016).”
Learning Optimal Causal Graphs with Exact Search (2018, background): “We consider both allowing cycles (assuming absence of self-loops as they are unidentifiable from (in)dependence constraints (Lacerda et al., 2008; Hyttinen et al., 2012)) and assuming acyclicity; in the latter case the causal graphs are semi-Markovian graphs (Pearl, 2000).”