Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Equivalence and synthesis of causal models
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
A graphical characterization of the largest chain graphs
International Journal of Approximate Reasoning
Causal discovery from a mixture of experimental and observational data
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
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Because causal learning from observational data cannot avoid the inherent indistinguishability for causal structures that have the same Markov properties, this paper discusses causal structure learning within a Markov equivalence class. We present that the additional causal information about a given variable and its adjacent variables, such as knowledge from experts or data from randomization experiments, can refine the Markov equivalence class into some smaller constrained equivalent subclasses, and each of which can be represented by a chain graph. Those sequential characterizations of subclasses provide an approach for learning causal structures. According to the approach, an iterative partition of the equivalent class can be made with data from randomization experiments until the exact causal structure is identified.