Score-based causal discovery
In this section, we’ll introduce score-based methods for causal discovery. We’ll discuss the mechanics of the GES algorithm and implement it using gCastle.
Tabula rasa – starting fresh
The very first step of the PC algorithm was to build a fully-connected graph. GES starts on the other end of the spectrum, but first things first.
GES is a two-stage procedure. First, it generates the edges, then it prunes the graph.
The algorithm starts with a blank slate – an entirely disconnected graph – and iteratively adds new edges. At each step, it computes a score that expresses how well a new graph models the observed distribution, and at each step, the edge that leads to the highest score is added to the graph.
When no more improvement can be achieved, the pruning phase begins. In this phase, the algorithm removes edges iteratively and checks for score improvement. The phase continues until no further improvement...
