Instrumental variable analysis is an approach for obtaining causal inferences on 
the effect of an exposure (risk factor) on an outcome from observational data. 
It has gained in popularity over the past decade with the use of genetic 
variants as instrumental variables, known as Mendelian randomization. An 
instrumental variable is associated with the exposure, but not associated with 
any confounder of the exposure-outcome association, nor is there any causal 
pathway from the instrumental variable to the outcome other than via the 
exposure. Under the assumption that a single instrumental variable or a set of 
instrumental variables for the exposure is available, the causal effect of the 
exposure on the outcome can be estimated. There are several methods available 
for instrumental variable estimation; we consider the ratio method, two-stage 
methods, likelihood-based methods, and semi-parametric methods. Techniques for 
obtaining statistical inferences and confidence intervals are presented. The 
statistical properties of estimates from these methods are compared, and 
practical advice is given about choosing a suitable analysis method. In 
particular, bias and coverage properties of estimators are considered, 
especially with weak instruments. Settings particularly relevant to Mendelian 
randomization are prioritized in the paper, notably the scenario of a continuous 
exposure and a continuous or binary outcome.