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Incorporation of Latent Variables in Causal Discovery Using Experimental Data

Discovering the causal structure behind a body of statistical data is a difficult task. One of the main classes of algorithms used to learn causal structures, known as constraint-based algorithms, currently is only able to find partial data about a causal model. Some arcs are left partially or completely undirected, and very little information is determined about the structure of latent variables affecting the model. This paper examines how much additional information can be determined about the causal structure and the presence of latent variables, through the incorporation of experimental intervention on specific variables. These intervention techniques are then incorporated into the IC-Intervention algorithm, with experimental application of the algorithm giving promising results.