  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
$k$. The algorithm is an adaptation of the well-known FCI algorithm by (Spirtes
et al., 2000) that is also sound and complete, but has worst case complexity
exponential in $N$.
