Comparative Study of Causal Discovery Methods for Cyclic Models with
  Hidden Confounders
Boris Lorbeer Mustafa Mohsen
Nowadays, the need for causal discovery is ubiquitous. A better understanding
of not just the stochastic dependencies between parts of a system, but also the
actual cause-effect relations, is essential for all parts of science. Thus, the
need for reliable methods to detect causal directions is growing constantly. In
the last 50 years, many causal discovery algorithms have emerged, but most of
them are applicable only under the assumption that the systems have no feedback
loops and that they are causally sufficient, i.e. that there are no unmeasured
subsystems that can affect multiple measured variables. This is unfortunate
since those restrictions can often not be presumed in practice. Feedback is an
integral feature of many processes, and real-world systems are rarely
completely isolated and fully measured. Fortunately, in recent years, several
techniques, that can cope with cyclic, causally insufficient systems, have been
developed. And with multiple methods available, a practical application of
those algorithms now requires knowledge of the respective strengths and
weaknesses. Here, we focus on the problem of causal discovery for sparse linear
models which are allowed to have cycles and hidden confounders. We have
prepared a comprehensive and thorough comparative study of four causal
discovery techniques: two versions of the LLC method [10] and two variants of
the ASP-based algorithm [11]. The evaluation investigates the performance of
those techniques for various experiments with multiple interventional setups
and different dataset sizes.