Causal inference methods for systematic reviews

Abstract

Systematic reviews are frequently used to ask and answer causal questions, and decisions made throughout the review process can introduce bias because of the causal relationships between variables of interest, even when the question being asked is not itself causal. However, current systematic review best practices do not consider most of these impacts. The author presents an introduction to the basic principles and terminology of causal inference as apply to systematic reviews and discusses how some simple causal inference concepts can be used to create clearer and less biased research questions, search strategies, eligibility criteria, and data analysis. By taking into account confounder and collider bias, and by paying careful attention to the differences in assumptions made in intervention- and non-intervention-based reviews, health science librarians can aid systematic review teams in minimizing the risk of bias, and in appropriately analyzing and assessing that risk.