Causal network discovery by iterative conditioning: comparison of algorithms

Understand

Estimating causal interactions in complex dynamical systems is an important problem encountered in many fields of current science.

• While a theoretical solution for detecting the causal interactions has been previously formulated in the framework of prediction improvement, it generally requires the computation of high-dimensional information functionals -- a situation invoking the curse of dimensionality with increasing network size.
• Recently, several methods have been proposed to alleviate this problem, based on iterative procedures for assessment of conditional (in)dependences.
• In the current work, we bring a comparison of several such prominent approaches.