A Local Method for Identifying Causal Relations under Markov Equivalence

TL;DR

This paper introduces a local method for determining causal relationships between variables in DAGs, using local structure and independence tests, which is efficient and effective compared to existing methods.

Contribution

It presents a novel local approach with graphical conditions and an algorithm for causal inference under Markov equivalence, focusing on local structures.

Findings

The local algorithm accurately identifies causal relations in simulations.

It outperforms state-of-the-art methods in efficiency and effectiveness.

The method relies on local structure and statistical independence tests.

Abstract

Causality is important for designing interpretable and robust methods in artificial intelligence research. We propose a local approach to identify whether a variable is a cause of a given target under the framework of causal graphical models of directed acyclic graphs (DAGs). In general, the causal relation between two variables may not be identifiable from observational data as many causal DAGs encoding different causal relations are Markov equivalent. In this paper, we first introduce a sufficient and necessary graphical condition to check the existence of a causal path from a variable to a target in every Markov equivalent DAG. Next, we provide local criteria for identifying whether a variable is a cause/non-cause of a target based only on the local structure instead of the entire graph. Finally, we propose a local learning algorithm for this causal query via learning the local structure of the variable and some additional statistical independence tests related to the target. Simulation studies show that our local algorithm is efficient and effective, compared with other state-of-art methods.