Learning Relational Causal Models with Cycles through Relational Acyclification

TL;DR

This paper introduces relational acyclification, a novel method enabling causal discovery in cyclic relational models, demonstrating that under certain assumptions, existing algorithms can be extended to handle cycles.

Contribution

The paper proposes relational acyclification, a new operation that allows existing causal discovery algorithms to learn cyclic relational causal models.

Findings

Relational acyclification enables reasoning about cycles in relational models.

Under $\sigma$-faithfulness, RCD is sound and complete for cyclic models.

Experimental results support the effectiveness of the proposed approach.

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 $\sigma$-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.