Title: Combining Experiments to Discover Linear Cyclic Models with Latent Variables
Abstract: We present an algorithm to infer causal relations between a set of measured variables on the basis of experiments on these variables. The algorithm assumes that the causal relations are linear, but is otherwise completely general: It provides consistent estimates when the true causal structure contains feedback loops and latent variables, while the experiments can involve surgical or ‘soft’ interventions on one or multiple variables at a time. The algorithm is ‘online’ in the sense that it combines the results from any set of available experiments, can incorporate background knowledge and resolves conicts that arise from combining results from dierent experiments. In addition we provide a necessary and sucient condition that (i) determines when the algorithm can uniquely return the true graph, and (ii) can be used to select the next best experiment until this condition is satised. We demonstrate the
Publication Year: 2010
Publication Date: 2010-03-31
Language: en
Type: article
Access and Citation
Cited By Count: 40
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