Proof Supplement - Learning Sparse Causal Models is not NP-hard (UAI2013)

6 Nov 2014Tom ClaassenJoris M. MooijTom Heskes

This article contains detailed proofs and additional examples related to the UAI-2013 submission `Learning Sparse Causal Models is not NP-hard'. It describes the FCI+ algorithm: a method for sound and complete causal model discovery in the presence of latent confounders and/or selection bias, that has worst case polynomial complexity of order $N^{2(k+1)}$ in the number of independence tests, for sparse graphs over $N$ nodes, bounded by node degree $k$... (read more)

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