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Finding Latent Causes in Causal Networks: an Efficient Approach Based on Markov Blankets
Jean-Philippe Pellet · Andre Elisseeff

Mon Dec 08 08:45 PM -- 12:00 AM (PST) @

Causal structure-discovery techniques usually assume that all causes of more than one variable are observed. This is the so-called causal sufficiency assumption. In practice, it is untestable, and often violated. In this paper, we present an efficient causal structure-learning algorithm, suited for causally insufficient data. Similar to algorithms such as IC* and FCI, the proposed approach drops the causal sufficiency assumption and learns a structure that indicates (potential) latent causes for pairs of observed variables. Assuming a constant local density of the data-generating graph, our algorithm makes a quadratic number of conditional-independence tests w.r.t. the number of variables. We show with experiments that our algorithm is comparable to the state-of-the-art FCI algorithm in accuracy, while being several orders of magnitude faster on large problems. We conclude that MBCS* makes a new range of causally insufficient problems computationally tractable.

Author Information

Jean-Philippe Pellet (IBM Zurich Research Lab)
Andre Elisseeff (IBM Zurich Research Lab)

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