Year: 2024
Type: article
Abstract: Instrumental variables (IV) analysis is a powerful, but fragile, tool for drawing causal inferences from observational data. Sociologists increasingly turn to this strategy in settings where unmeasured confounding between the treatment and outcome is likely. This paper reviews the assumptions required for IV and the consequences of violating them, focusing on sociological applications. We highlight three methodological problems IV faces: (i) identification bias, an asymptotic bias from assumption violations; (ii) estimation bias, a finite-sample bias that persists even when assumptions hold; and (iii) type-M error, the 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. We survey IV papers from 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.
Source: Sociological Methods & Research
Authors: Chris Felton, Brandon M. Stewart
Institutions: Harvard University, Princeton University
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,700
Award: G3513152605
OpenAlex ID: W4401467303