  Unlike other techniques of causality inference, the use of valid instrumental
variables can deal with unobserved sources of both variable errors, variable
omissions, and sampling bias, and still arrive at consistent estimates of
average treatment effects. The only problem is to find the valid instruments.
Using the definition of Pearl (2009) of valid instrumental variables, a formal
condition for validity can be stated for variables in generalized linear causal
models. The condition can be applied in two different ways: As a tool for
constructing valid instruments, or as a foundation for testing whether an
instrument is valid. When perfectly valid instruments are not found, the
squared bias of the IV-estimator induced by an imperfectly valid instrument --
estimated with bootstrapping -- can be added to its empirical variance in a
mean-square-error-like reliability measure.
