[edit]

# Learning Optimal Cyclic Causal Graphs from Interventional Data

*Proceedings of the 10th International Conference on Probabilistic Graphical Models*, PMLR 138:365-376, 2020.

#### Abstract

We consider causal discovery in a very general setting
involving non-linearities, cycles and several experimental datasets in
which only a subset of variables are recorded. Recent approaches
combining constraint-based causal discovery, weighted independence
constraints and exact optimization have shown improved accuracy.
However, they have mainly focused on the d-separation criterion, which
is theoretically correct only under strong assumptions such as
linearity or acyclicity. The more recently introduced sigma-separation
criterion for statistical independence enables constraint-based causal
discovery for non-linear relations over cyclic structures. In this work we
make several contributions in this setting. (i) We generalize bcause, a
recent exact branch-and-bound causal discovery approach, to this
setting, integrating support for the sigma-separation criterion and
several interventional datasets. (ii) We empirically analyze different
schemes for weighting independence constraints in terms of accuracy
and runtimes of bcause. (iii) We provide improvements to a previous
declarative answer set programming (ASP) based approach for causal
discovery employing the sigma-separation criterion, and empirically
evaluate bcause and the refined ASP-approach.