Bivariate Causal Discovery and its Applications to Gene Expression and Imaging Data Analysis

TL;DR

This paper introduces a novel approach for bivariate causal discovery using the additive noise model, addressing the challenge of inferring causality between two variables in genetic and imaging data analysis.

Contribution

It proposes the independence of cause and mechanism principle, applying ANM for causal inference in bivariate data, and evaluates its effectiveness through simulations and real data applications.

Findings

ANM effectively distinguishes causal directions in simulated data.

Application to gene regulatory networks demonstrates practical utility.

Analysis of trait-imaging data reveals different causal and association scenarios.

Abstract

The mainstream of research in genetics, epigenetics and imaging data analysis focuses on statistical association or exploring statistical dependence between variables. Despite their significant progresses in genetic research, understanding the etiology and mechanism of complex phenotypes remains elusive. Using association analysis as a major analytical platform for the complex data analysis is a key issue that hampers the theoretic development of genomic science and its application in practice. Causal inference is an essential component for the discovery of mechanical relationships among complex phenotypes. Many researchers suggest making the transition from association to causation. Despite its fundamental role in science, engineering and biomedicine, the traditional methods for causal inference require at least three variables. However, quantitative genetic analysis such as QTL, eQTL, mQTL, and genomic-imaging data analysis requires exploring the causal relationships between two variables. This paper will focus on bivariate causal discovery. We will introduce independence of cause and mechanism (ICM) as a basic principle for causal inference, algorithmic information theory and additive noise model (ANM) as major tools for bivariate causal discovery. Large-scale simulations will be performed to evaluate the feasibility of the ANM for bivariate causal discovery. To further evaluate their performance for causal inference, the ANM will be applied to the construction of gene regulatory networks. Also, the ANM will be applied to trait-imaging data analysis to illustrate three scenarios: presence of both causation and association, presence of association while absence of causation, and presence of causation, while lack of association between two variables.