A Unified Experiment Design Approach for Cyclic and Acyclic Causal Models

Understand

We study experiment design for unique identification of the causal graph of a simple SCM, where the graph may contain cycles.

• The presence of cycles in the structure introduces major challenges for experiment design as, unlike acyclic graphs, learning the skeleton of causal graphs with cycles may not be possible from merely the observational distribution.
• Furthermore, intervening on a variable in such graphs does not necessarily lead to orienting all the edges incident to it.
• In this paper, we propose an experiment design approach that can learn both cyclic and acyclic graphs and hence, unifies the task of experiment design for both types of graphs.