Introduction to Instrumental Variable Analysis

Abstract

In theory, instrumental variable (IV) analysis, like randomized controlled trials, can adjust for measured and unmeasured confounders. IVs need to meet the following three conditions: (i) they are associated with treatment assignment; (ii) they have no direct association with the outcome and are associated with the outcome exclusively through the treatment; and (iii) they are not associated with any of the measured confounders. Studies have presented several types of IV, including preferences of the facility or physician, differential distance, and days of the week. Two types of estimation method have been introduced: two-stage least squares and two-stage residual inclusion. The assumption of monotonicity limits the generalizability of estimates of causal effects in IV analysis because the target population of IV analysis is “compliers” (those who always comply with the assigned treatment). IV analysis using two or more IVs is feasible but requires the overidentifying restriction test. Despite several limitations, IV analysis is a feasible option that may be used for causal inference in comparative effectiveness studies using retrospective observational data.