Paper Info
Title
Causal Consistency Of Structural Equation Models
Abstract
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) micro-level models versus macro-level 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.
Year
Venue
DocType
2017
CONFERENCE ON UNCERTAINTY IN ARTIFICIAL INTELLIGENCE (UAI2017)
Conference
Volume
ISSN
Citations 
abs/1707.00819
Proceedings of the Annual Conference on Uncertainty in Artificial Intelligence, UAI 2017
1
PageRank 
References 
Authors
0.41
0
7
Name
Order
Citations
PageRank
Paul Rubenstein1114.28
Sebastian Weichwald2163.84
Stephan Bongers332.49
Joris M. Mooij467950.48
Dominik Janzing572365.30
Moritz Grosse-Wentrup627324.44
Bernhard Schölkopf7231203091.82