Handle with Care: A Sociologist's Guide to Causal Inference with Instrumental Variables

Abstract

Instrumental variables (IV) analysis is a powerful, but fragile, tool for drawing causal inferences from observational data. Sociologists have increasingly turned to this strategy in settings where unmeasured confounding between the treatment and outcome is likely. This paper provides an introduction to the assumptions required for IV and consequences of their violations for applications in sociology. We review three methodological problems IV faces: identification bias (asymptotic bias from assumption violations), estimation bias (finite-sample bias that persists even when assumptions hold), and type-M error (exaggeration of effects given statistical significance). In each case, we emphasize how weak instruments exacerbate these problems and make results sensitive to minor violations of assumptions. Our discussion is informed by a new survey of IV papers published in top sociology journals showing that assumptions often go unstated and robust uncertainty measures are rarely used. We provide a practical checklist to show how IV, despite its fragility, can still be useful when handled with care.