e-Article
More (Adjustment) Is Not Always Better: How Directed Acyclic Graphs Can Help Researchers Decide Which Covariates to Include in Models for the Causal Relationship between an Exposure and an Outcome in Observational Research.
Document Type
Editorial
Author
Source
Subject
*CAUSAL models
*LOW birth weight
*
Language
ISSN
0033-3190
Abstract
Causal DAGs can clarify how adjustment for a given covariate might impact bias, by providing a simple way to visualize assumptions about the statistical relationships between the exposure, outcome, and covariates in question. Keywords: Causal inference; Bias; Directed acyclic graph; DAG; Methods EN Causal inference Bias Directed acyclic graph DAG Methods 289 298 10 08/07/21 20210901 NES 210901 Introduction When constructing a model for an outcome of interest (e.g., a linear regression model), the choice of covariates to be included depends in part on the researcher's aims. Depending on which of these paths is present, including maternal smoking as a covariate (i.e., in the causal model for the effect of preterm birth on ADHD) could increase bias, reduce bias, or both. [Extracted from the article]