Instrumental variables (IVs) are a popular and powerful tool for
estimating causal effects in the presence of unobserved confounding. However,
classical methods rely on strong assumptions such as the exclusion criterion,
which states that instrumental effects must be entirely mediated by
treatments. In the so-called "leaky" IV setting, candidate instruments are
allowed to have some direct influence on outcomes, rendering the average
treatment effect (ATE) unidentifiable. But with limits on the amount of
information leakage, we may still recover sharp bounds on the ATE, providing
partial identification. This package implements methods for ATE bounding in
the leaky IV setting with linear structural equations. For details, see Watson
et al. (2024)