Local Causal Structure Learning in the Presence of Latent Variables

TL;DR

This paper introduces a novel method for local causal structure learning that accounts for latent variables, using m-separation and V-structures to improve accuracy in real-world observational data.

Contribution

It develops a theoretically grounded approach with stop rules for identifying direct causes and effects, bridging local and global causal structure learning in the presence of latent variables.

Findings

The method is effective on synthetic data.

The approach demonstrates efficiency on real-world data.

Theoretical guarantees hold under standard assumptions.

Abstract

Discovering causal relationships from observational data, particularly in the presence of latent variables, poses a challenging problem. While current local structure learning methods have proven effective and efficient when the focus lies solely on the local relationships of a target variable, they operate under the assumption of causal sufficiency. This assumption implies that all the common causes of the measured variables are observed, leaving no room for latent variables. Such a premise can be easily violated in various real-world applications, resulting in inaccurate structures that may adversely impact downstream tasks. In light of this, our paper delves into the primary investigation of locally identifying potential parents and children of a target from observational data that may include latent variables. Specifically, we harness the causal information from m-separation and V-structures to derive theoretical consistency results, effectively bridging the gap between global and local structure learning. Together with the newly developed stop rules, we present a principled method for determining whether a variable is a direct cause or effect of a target. Further, we theoretically demonstrate the correctness of our approach under the standard causal Markov and faithfulness conditions, with infinite samples. Experimental results on both synthetic and real-world data validate the effectiveness and efficiency of our approach.