  Instrumental variable methods provide useful tools for inferring causal
effects in the presence of unmeasured confounding. To apply these methods with
large-scale data sets, a major challenge is to find valid instruments from a
possibly large candidate set. In practice, most of the candidate instruments
are often not relevant for studying a particular exposure of interest.
Moreover, not all relevant candidate instruments are valid as they may directly
influence the outcome of interest. In this article, we propose a data-driven
method for causal inference with many candidate instruments that addresses
these two challenges simultaneously. A key component of our proposal is a novel
resampling method, which constructs pseudo variables to remove irrelevant
candidate instruments having spurious correlations with the exposure. Synthetic
data analyses show that the proposed method performs favourably compared to
existing methods. We apply our method to a Mendelian randomization study
estimating the effect of obesity on health-related quality of life.
