Marcel Wienöbst, Maciej Liskiewicz:
Recovering Causal Structures from Low-Order Conditional Independencies.
In Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI'20), New York, New York USA, S. 10302-10309.
AAAI Press,
2020.
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One of the common obstacles for learning causal models from
data is that high-order conditional independence (CI) relationships
between random variables are difficult to estimate.
Since CI tests with conditioning sets of low order can be performed
accurately even for a small number of observations, a reasonable
approach to determine casual structures is to base merely on the
low-order CIs. Recent research has confirmed that, e.g. in the case of
sparse true causal models, structures learned even from zero- and
first-order conditional independencies yield good approximations
of the models. However, a challenging task here is to provide
methods that faithfully explain a given set of low-order CIs.
In this paper we propose an algorithm which, for a given set
of conditional independencies of order less or equal to $k$,
where $k$ is a small fixed number, computes a faithful graphical
representation of the given set. Our results complete and generalize
the previous work on learning from pairwise marginal independencies.
Moreover, they enable to improve upon the 0-1 graph model which,
e.g. is heavily used in the estimation of genome networks.
