Learning Relational Causal Models with Cycles through Relational Acyclification

Ragib Ahsan, David Arbour, Elena Zheleva

Abstract:

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 graphical models, relational causal models,
can represent and reason about complex dynamic systems exhibiting such cycles or feedback loops. Existing cyclic causal
discovery algorithms for learning causal models from observational data assume that the data instances are independent
and identically distributed which makes them unsuitable for
relational causal models. At the same time, causal discovery
algorithms for relational causal models assume acyclicity. In
this work, we examine the necessary and sufficient conditions
under which a constraint-based relational causal discovery
algorithm is sound and complete for cyclic relational causal
models. We introduce relational acyclification, an operation
specifically designed for relational models that enables reasoning about the identifiability of cyclic relational causal models.
We show that under the assumptions of relational acyclification
and σ-faithfulness, the relational causal discovery algorithm
RCD (Maier et al. 2013) is sound and complete for cyclic
models. We present experimental results to support our claim.