Structure Learning of Linear Gaussian Structural Equation Models with Weak Edges

Understand

We consider structure learning of linear Gaussian structural equation models with weak edges.

• Since the presence of weak edges can lead to a loss of edge orientations in the true underlying CPDAG, we define a new graphical object that can contain more edge orientations.
• We show that this object can be recovered from observational data under a type of strong faithfulness assumption.
• We present a new algorithm for this purpose, called aggregated greedy equivalence search (AGES), that aggregates the solution path of the greedy equivalence search (GES) algorithm for varying values of the penalty parameter.