From muddied waters to causal clarity? The use and limitations of Instrumental Variables in health economics

Abstract

Randomisation is rightly regarded as the gold standard is assessing the causal effect of an intervention or risk factor on an outcome. However in many cases, randomisation is not feasible, desirable or practical. In such cases observational data may be the only source of empirical evidence, but comes with a distinct health warning that bias may occur if potential confounding factors are not adequately addressed in the statistical analysis. Instrumental Variables are a ‘signature’ technique of economics, rarely used elsewhere, which effectively propose exploiting fortunate coincidences in the data generating process to resolve omitted variable bias. The key element is whether it’s possible to identify at least one variable which is correlated with the causal variable of interest, but not directly related to the outcome. From the origins of John Snow’s 1855 seminal work in identifying the mode of communication of cholera we chart the use of IVs in economics and health economics specifically, outlying the theory and the key requirements for successful application and the problems of weak or implausible instruments. We show that although a neat and promising concept, IVs should be used with caution and only applied where circumstances permit identification of plausible instruments