Identification of Strong Edges in AMP Chain Graphs
This work addresses a specific issue in causal inference for researchers using AMP chain graphs, representing an incremental advancement in graph theory methods.
The paper tackles the problem of identifying which edges in an essential graph of AMP chain graphs are strong, meaning they are shared by all members of the Markov equivalence class, and demonstrates how this enables bounding causal effects when the true graph is unknown.
The essential graph is a distinguished member of a Markov equivalence class of AMP chain graphs. However, the directed edges in the essential graph are not necessarily strong or invariant, i.e. they may not be shared by every member of the equivalence class. Likewise for the undirected edges. In this paper, we develop a procedure for identifying which edges in an essential graph are strong. We also show how this makes it possible to bound some causal effects when the true chain graph is unknown.