Identifying Causal Structure in Large-Scale Kinetic Systems

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Causal Models for Dynamical Systems (2020, methodology): “…methodology and several extensions of SCMs have been proposed [Wiener 1956, Granger 1969, Schreiber 2000, White and Lu 2010, Hyttinen et al. 2013, Peters et al. 2013, Pfister et al. 2018b], mostly con sidering discrete time models such as vector autoregressive models [Lütkepohl 2007], for example.”
Causality for Machine Learning (2019, methodology): “Recent progress in (conditional) independence testing heavily relies on kernel function classes to represent probability distributions in reproducing kernel Hilbert spaces (Gretton et al., 2005a,b; Fukumizu et al., 2008; Zhang et al., 2011; Pfister et al., 2018b; Chalupka et al., 2018).”
Fast Gaussian Process Based Gradient Matching for Parameter Identification in Systems of Nonlinear ODEs (2018, methodology): “The advantages of the method have been well established, with applications in biomedical domains e.g., systems biology or neuro-science (Babtie et al., 2014; Macdonald and Husmeier, 2015; Pﬁster et al., 2018).”