A gentle introduction to instrumental variables

Abstract

Instrumental variables (IV) is a central strategy for identifying causal effects in absence of randomized experiments. Clinicians and epidemiologists may find the intuition of IV easy to grasp by comparison to randomized experiments. Randomization is an ideal IV because treatment is assigned randomly, and hence unaffected by everything else. IV methods in nonexperimental settings mimic a randomized experiment by using a source of “as good as” random variation in treatment instead. The main challenge with IV designs is to find IVs that are as good as randomization. Discovering potential IVs require substantive knowledge and an understanding of design principles. Moreover, IV methods recover causal effects for a subset of the population who take treatment when induced by the IV. Sometimes these estimates are informative, other times their relevance is questionable. We provide an introduction to IV methods in clinical epidemiology. First, we introduce the main principles and assumptions. Second, we present practical examples based on Mendelian randomization and provider preference and refer to other common IVs in health. Third, practical steps in IV analysis are presented. Fourth, the promise and perils of IV methods are discussed. Finally, we suggest further readings.