Measurement bias: a structural perspective

TL;DR

This paper introduces a new structural perspective on measurement bias using DAGs, clarifying its mechanisms and sources in causal inference, especially in measurement systems and effect estimation.

Contribution

It proposes a novel DAG-based framework for understanding measurement bias, including effects of measurement system selection and bidirectional differential misclassification.

Findings

New DAG structures clarify measurement bias mechanisms.

Identifies sources of bias in effect estimation due to measurement issues.

Highlights the concept of reverse causality at the measurement level.

Abstract

The causal structure for measurement bias (MB) remains controversial. Aided by the Directed Acyclic Graph (DAG), this paper proposes a new structure for measuring one singleton variable whose MB arises in the selection of an imperfect I/O device-like measurement system. For effect estimation, however, an extra source of MB arises from any redundant association between a measured exposure and a measured outcome. The misclassification will be bidirectionally differential for a common outcome, unidirectionally differential for a causal relation, and non-differential for a common cause between the measured exposure and the measured outcome or a null effect. The measured exposure can actually affect the measured outcome, or vice versa. Reverse causality is a concept defined at the level of measurement. Our new DAGs have clarified the structures and mechanisms of MB.