Relational Causal Discovery with Latent Confounders

TL;DR

This paper introduces RelFCI, a novel causal discovery algorithm tailored for relational data with hidden confounders, addressing limitations of existing methods that assume i.i.d. data or causal sufficiency.

Contribution

RelFCI extends causal discovery to relational domains with latent confounders, providing soundness, completeness, and new graphical models for such complex data.

Findings

RelFCI accurately identifies causal structures in relational data with hidden confounders.

Experimental results show RelFCI outperforms existing algorithms in complex relational scenarios.

Theoretical guarantees confirm soundness and completeness of the proposed method.

Abstract

Estimating causal effects from real-world relational data can be challenging when the underlying causal model and potential confounders are unknown. While several causal discovery algorithms exist for learning causal models with latent confounders from data, they assume that the data is independent and identically distributed (i.i.d.) and are not well-suited for learning from relational data. Similarly, existing relational causal discovery algorithms assume causal sufficiency, which is unrealistic for many real-world datasets. To address this gap, we propose RelFCI, a sound and complete causal discovery algorithm for relational data with latent confounders. Our work builds upon the Fast Causal Inference (FCI) and Relational Causal Discovery (RCD) algorithms and it defines new graphical models, necessary to support causal discovery in relational domains. We also establish soundness and completeness guarantees for relational d-separation with latent confounders. We present experimental results demonstrating the effectiveness of RelFCI in identifying the correct causal structure in relational causal models with latent confounders.