Restructuring Dynamic Causal Systems in Equilibrium

By Denver Dash. 2005

56 citations recorded by the source corpus.

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From Deterministic ODEs to Dynamic Structural Causal Models (2016, background): “…from more fundamental models by showing how, under strong assumptions, SCMs can be derived from an underlying discrete time difference equation or continuous time ODE (Iwasaki and Simon, 1994; Dash, 2005; Lacerda et al., 2008; Voortman et al., 2010; Mooij et al., 2013; Sokol and Hansen, 2014).”
From Ordinary Differential Equations to Structural Causal Models: the deterministic case (2013, methodology): “…option is to assume an underlying discrete-time dynamical system, in which the structural equations are used as ﬁxed point equations (Spirtes, 1995; Dash, 2005; Lacerda et al., 2008; Mooij et al., 2011; Hyttinen et al., 2012), i.e., they are used as update rules to calculate the values at time t…”
The max-min hill-climbing Bayesian network structure learning algorithm (2006, background): “In addition, while still controversial (see Dash, 2005 for a recent discussion) learning Bayesian networks is being used for inferring possible causal relations since, under certain conditions (Spirtes, Glymour & Scheines, 2000) the edges in the graph of a Bayesian network have causal semantics…”
Discovering Cyclic Causal Models by Independent Components Analysis (2008, background): “As Dash has pointed out [4], there are cases where simultaneous equations have a different graph than the underlying dynamical equations, and hence the graph that represents the simultaneous equations cannot be used to predict the effects of a manipulation of the underlying dynamical system.”
Mesochronal Structure Learning (2015, background): “There has been very little prior work on the problem of structure learning from undersampled time series data, though there have been important prior explorations of learning when the measurement and sys-tem timescales diverge, or when causal inﬂuences operate on multiple timescales [3, 5, 9].”
From Random Differential Equations to Structural Causal Models: the stochastic case (2018, background): “They can be derived from an underlying discrete-time or continuous-time dynamical system (Fisher, 1970; Iwasaki and Simon, 1994; Dash, 2005; Lacerda et al., 2008; Mooij et al., 2013).”