Brief Introduction to Causal Compositional Models

  • Radim JiroušekEmail author
Part of the Studies in Computational Intelligence book series (SCI, volume 622)


When applying probabilistic models to support decision making processes, the users have to strictly distinguish whether the impact of their decision changes the considered situation or not. In the former case it means that they are planing to make an intervention, and its respective impact cannot be estimated from a usual stochastic model but one has to use a causal model. The present paper thoroughly explains the difference between conditioning, which can be computed from both usual stochastic model and a causal model, and computing the effect of intervention, which can only be computed from a causal model. In the paper a new type of causal models, so called compositional causal models are introduced. Its great advantage is that both conditioning and the result of intervention are computed in very similar ways in these models. On an example, the paper illustrates that like in Pearl’s causal networks, also in the described compositional models one can consider models with hidden variables.


Bayesian Network Causal Relation Conditional Distribution Causal Model Hide Variable 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was supported in part by the National Science Foundation of the Czech Republic by grant no. GACR 15-00215S.


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© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Faculty of managementUniversity of Economics, PragueJindřichův HradecCzech Republic

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