Encoding expert knowledge
Combining expert knowledge with automated methods can be incredibly beneficial. It can help algorithms learn while inspiring human stakeholders to deepen their own insights and understanding of their environments and processes.
In this section, we’ll demonstrate how to incorporate expert knowledge into the workflow of our causal discovery algorithms.
By the end of this section, you’ll be able to translate expert knowledge into the language of graphs and pass it to causal discovery algorithms.
What is expert knowledge?
In this section, we think about expert knowledge as an umbrella term for any type of knowledge or insight that we’re willing to accept as valid.
From the algorithmic point of view, we can think of expert knowledge as a strong (but usually local) prior. We encode expert knowledge by freezing one or more edges in the graph. The model treats these edges as existing and adapts their behavior accordingly.
