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A generalized back-door criterion
We generalize Pearl's back-door criterion for directed acyclic graphs (DAGs) to more general types of graphs that describe Markov equivalence classes of DAGs and/or allow for arbitrarily many hidden variables.
Order-independent constraint-based causal structure learning
This algorithm is known to be order-dependent, in the sense that the output can depend on the order in which the variables are given.
Learning high-dimensional directed acyclic graphs with latent and selection variables
However, we prove that any causal information in the output of RFCI is correct in the asymptotic limit.
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