Marginal causal consistency in constraint-based causal learning
(2016)Abstract
Maximal Ancestral Graphs (MAGs) are probabilistic graphical models that can model the distribution and causal properties of a set of variables in the presence of latent confounders. They
are closed under marginalization. Invariant pairwise features of a class of Markov equivalent
MAGs can be learnt from observational data sets
using the FCI algorithm and its variations (such
as conservative FCI and order independent FCI).
We investigate the consistency of causal features
(causal ancestry relations) obtained by FCI in
different marginals of a single data set. In principle, the causal relationships identified by FCI
on a data set D measuring a set of variables V
should not conflict the output of FCI on marginal
data sets including only subsets of V. In practice, however, FCI is prone to error propagation,
and running FCI in different marginals results
in inconsistent causal predictions. We introduce
the term of marginal causal consistency to denote the consistency of causal relationships when
learning marginal distributions, and investigate
the marginal causal consistency of different FCI
variations.Results indicate that marginal causal
consistency varies for different algorithms, and
is also sensitive to network density and marginal
size
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