Causal Discovery with Deductive Reasoning: One Less Problem

Jonghwan Kim, Inwoo Hwang, Sanghack Lee

Keywords: causal discovery, deductive reasoning

Abstract: Constraint-based causal discovery algorithms aim to extract causal relationships between variables of interest by using conditional independence tests (CITs). However, CITs with large conditioning sets often lead to unreliable results due to their low statistical power, propagating errors throughout the course of causal discovery. As the reliability of CITs is crucial for their practical applicability, recent approaches rely on either tricky heuristics or complicated routines with high computational costs to tackle inconsistent test results. Against this background, we propose a principled, simple, yet effective method, coined \textsc{deduce-dep}, which corrects unreliable conditional independence statements by replacing them with deductively reasoned results from lower-order CITs. An appealing property of \textsc{deduce-dep} is that it can be seamlessly plugged into existing constraint-based methods and serves as a modular subroutine. In particular, we showcase the integration of \textsc{deduce-dep} into representative algorithms such as HITON-PC and PC, illustrating its practicality. Empirical evaluation demonstrates that our method properly corrects unreliable CITs, leading to improved performance in causal structure learning.

List Of Authors: Kim, Jonghwan and Hwang, Inwoo and Lee, Sanghack

Code Url: https://github.com/snu-causality-lab/deduce-dep

Submission Number: 736