
MultiLevel CauseEffect Systems
We present a domaingeneral account of causation that applies to setting...
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Structural causal models for macrovariables in timeseries
We consider a bivariate time series (X_t,Y_t) that is given by a simple ...
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Abstracting Causal Models
We consider a sequence of successively more restrictive definitions of a...
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Causal models for dynamical systems
A probabilistic model describes a system in its observational state. In ...
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Structural Causal Models Are (Solvable by) Credal Networks
A structural causal model is made of endogenous (manifest) and exogenous...
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The Quasi cellular netsbased models of transport and logistic systems
There are many systems in different subjects such as industry, medicine,...
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Approximate Causal Abstraction
Scientific models describe natural phenomena at different levels of abst...
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Causal Consistency of Structural Equation Models
Complex systems can be modelled at various levels of detail. Ideally, causal models of the same system should be consistent with one another in the sense that they agree in their predictions of the effects of interventions. We formalise this notion of consistency in the case of Structural Equation Models (SEMs) by introducing exact transformations between SEMs. This provides a general language to consider, for instance, the different levels of description in the following three scenarios: (a) models with large numbers of variables versus models in which the `irrelevant' or unobservable variables have been marginalised out; (b) microlevel models versus macrolevel models in which the macrovariables are aggregate features of the microvariables; (c) dynamical time series models versus models of their stationary behaviour. Our analysis stresses the importance of well specified interventions in the causal modelling process and sheds light on the interpretation of cyclic SEMs.
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