Causality and Model Abstraction

By Y. Iwasaki, Herbert A. Simon. 1994

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Citations in the corpus

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).”
Caveats for Causal Reasoning with Equilibrium Models (2001, methodology): “In this shorthand graph temporal subscripts can be dropped and we use special dashed links, labelled integration links [5], to denote that a causal relationship is really occurring through a time slice.”
Ecological models based on people’s knowledge: a multi-step fuzzy cognitive mapping approach (2004, methodology): “Structural equation modeling (SEM) (also known as causal modeling, covariance structural modeling, LISREL and others) is based on the statistical model developed byJöreskog (1977) .”
Learning Why Things Change: The Difference-Based Causality Learner (2010, methodology): “This paper considers Diﬀerence-Based Causal Models (DBCMs), a class of discrete-time dynamic models inspired by Iwasaki and Simon [1994] that models all causation across time by means of diﬀerence equations driving change in the 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].”
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].”
The Next Generation of Research on IS Use: A Theoretical Framework of Delegation to and from Agentic IS Artifacts (2021, background): “Such models are abstractions used to understand causality and processes (Iwasaki and Simon 1994) and are representations of how variables are expected or predicted to interact (Russell and Norvig 2016), but may be opaque (Burrell 2016).”
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).”
UvA-DARE ( Digital Academic Repository ) Causal Consistency of Structural Equation Models (2017, unknown): “(b) Micro-level models versus macro-level models in which the macro-variables are aggregate features of the micro-variables [4, 5, 9, 11, 17]; e.”
Electric, Hybrid, and Fuel-Cell Vehicles: Architectures and Modeling (2010, background): “, integral causality) [52], [53], [64].”
Restructuring Dynamic Causal Systems in Equilibrium (2005, methodology): “It has been argued by Iwasaki and Simon [1994] that the causal relations governing a dynamic system can change as the time-scale of observation of the system is increased.”
Causal Discovery from Subsampled Time Series Data by Constraint Optimization (2016, methodology): “While information about time order is generally seen as simplifying causal analysis, time series data that undersamples the generating process can be misleading about the true causal connections (Dash and Druzdzel, 2001; Iwasaki and Simon, 1994).”