Mendelian Randomisation for Causal Inference in Observational Epidemiology
Nuala Sheehan, University of Leicester.
Assessing the effect of some modifiable exposure on a disease from observational data is
often important for informing health intervention policies. To be confident that a particular
intervention will actually be useful, it is necessary to ascertain that an observed association or
correlation between the exposure and disease means that the risk factor is causal for the
disease. Inferring causality from observational data is difficult as it is not always clear which
of two associated variables is the cause, which the effect, or whether both are common effects
of a third unobserved variable, or confounder. A possible approach to testing for or
estimating causal effects when confounding is believed to be present but not fully understood
is based on the method of instrumental variables which are widely used in econometrics. It
is known under the name of Mendelian randomisation, if the instrument is a genetic variant,
and has received a lot of attention in the epidemiological literature recently.
Testing for the presence of a causal effect is generally straightforward but point estimates are
only obtainable under additional parametric and distributional assumptions. Such
assumptions cannot be tested directly from the observable quantities and thus must be
justified from background or application-specific knowledge. Different IV estimators require
different assumptions and target different causal effects, some of which are not necessarily
meaningful in an observational setting. Problems particularly arise when the outcome of
interest is a binary indicator of disease status, for example, although there are special cases
where these can be satisfactorily addressed. A Bayesian approach could also be considered
but also requires assumptions that have to be justified.