Learning Relational Causal Models with Cycles through Relational Acyclification

In real-world phenomena which involve mutual influence or causal effects between interconnected units, equilibrium states are typically represented with cycles in graphical models. An expressive class of models, relational can represent and reason about complex dynamic systems exhibiting such feedback loops. Existing cyclic discovery algorithms for learning models from observational data assume that the instances independent identically distributed makes them unsuitable At same time, acyclicity. this work, we examine necessary sufficient conditions under a constraint-based algorithm is sound complete We introduce acyclification, an operation specifically designed enables reasoning identifiability show assumptions acyclification sigma-faithfulness, RCD present experimental results to support our claim.