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Learning semimarkovian causal models using experiments
 IN: PROCEEDINGS OF THE THIRD EUROPEAN WORKSHOP ON PROBABILISTIC GRAPHICAL MODELS , PGM
, 2006
"... SemiMarkovian causal models (SMCMs) are an extension of causal Bayesian networks for modeling problems with latent variables. However, there is a big gap between the SMCMs used in theoretical studies and the models that can be learned from observational data alone. The result of standard algorithms ..."
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SemiMarkovian causal models (SMCMs) are an extension of causal Bayesian networks for modeling problems with latent variables. However, there is a big gap between the SMCMs used in theoretical studies and the models that can be learned from observational data alone. The result of standard algorithms for learning from observations, is a complete partially ancestral graph (CPAG), representing the Markov equivalence class of maximal ancestral graphs (MAGs). In MAGs not all edges can be interpreted as immediate causal relationships. In order to apply stateoftheart causal inference techniques we need to completely orient the learned CPAG and to transform the result into a SMCM by removing noncausal edges. In this paper we combine recent work on MAG structure learning from observational data with causal learning from experiments in order to achieve that goal. More specifically, we provide a set of rules that indicate which experiments are needed in order to transform a CPAG to a completely oriented SMCM and how the results of these experiments have to be processed. We will propose an alternative representation for SMCMs that can easily be parametrised and where the parameters can be learned with classical methods. Finally, we show how this parametrisation can be used to develop methods to efficiently perform both probabilistic and causal inference.
Causal Graphical Models with Latent Variables: Learning and Inference
"... Several paradigms exist for modeling causal graphical models for discrete variables that can handle latent variables without explicitly modeling them quantitatively. Applying them to a problem domain consists of different steps: structure learning, parameter learning and using them for probabilisti ..."
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Several paradigms exist for modeling causal graphical models for discrete variables that can handle latent variables without explicitly modeling them quantitatively. Applying them to a problem domain consists of different steps: structure learning, parameter learning and using them for probabilistic or causal inference. We discuss two wellknown formalisms, namely semiMarkovian causal models and maximal ancestral graphs and indicate their strengths and limitations. Previously an algorithm has been constructed that by combining elements from both techniques allows to learn a semiMarkovian causal models from a mixture of observational and experimental data. The goal of this paper is to recapitulate the integral learning process from observational and experimental data and to demonstrate how different types of inference can be performed efficiently in the learned models. We will do this by proposing an alternative representation for semiMarkovian causal models.