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Accurate measurement of the marginal healthcare costs associated with different diseases and health conditions is important, especially for increasingly prevalent conditions such as obesity. However, existing observational study designs cannot identify the causal impact of disease on healthcare costs. This paper explores the possibilities for causal inference offered by Mendelian Randomization, a form of instrumental variable analysis that uses genetic variation as a proxy for modifiable risk exposures, to estimate the effect of health conditions on cost. Well-conducted genome-wide association studies provide robust evidence of the associations of genetic variants with health conditions or disease risk factors. The subsequent causal effects of these health conditions on cost can be estimated by using genetic variants as instruments for the health conditions. This is because the approximately random allocation of genotypes at conception means that many genetic variants are orthogonal to observable and unobservable confounders. Datasets with linked genotypic and resource use information obtained from electronic medical records or from routinely collected administrative data are now becoming available, and will facilitate this form of analysis. We describe some of the methodological issues that arise in this type of analysis, which we illustrate by considering how Mendelian Randomization could be used to estimate the causal impact of obesity, a complex trait, on healthcare costs. We describe some of the data sources that could be used for this type of analysis. We conclude by considering the challenges and opportunities offered by Mendelian Randomization for economic evaluation.