Paper accepted to PGM 2020

The work extends and improves the empirical performance of exact approaches to causal discovery in a very general setting.

The paper Learning Optimal Cyclic Causal Graphs from Interventional Data by Kari Rantanen, Antti Hyttinen, and Matti J√§rvisalo has been accepted for publications in the proceedings of the 10th International Conference on Probabilistic Graphical Models (PGM 2020).

The work considers 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 both non-linear relations and cyclic structures. The paper makes several contributions in this setting. (i) Generalizes 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) Empirically analyzes different schemes for weighting independence constraints in terms of accuracy and runtimes of bcause. (iii) Provides improvements to a previous answer set programming (ASP) based approach for causal discovery employing the sigma-separation criterion, and empirically evaluates bcause and the refined ASP-approach.