  In randomized experiments with non-compliance scholars have argued that the
complier average causal effect (CACE) ought to be the main causal estimand. The
literature on inference of the complier average treatment effect (CACE) has
focused on inference about the population CACE. However, in general individuals
in the experiments are volunteers. This means that there is a risk that
individuals partaking in a given experiment differ in important ways from a
population of interest. It is thus of interest to focus on the sample at hand
and have easy to use and correct procedures for inference about the sample
CACE. We consider a more general setting than in the previous literature and
construct a confidence interval based on the Wald estimator in the form of a
finite closed interval that is familiar to practitioners. Furthermore, with the
access of pre-treatment covariates, we propose a new regression adjustment
estimator and associated methods for constructing confidence intervals. Finite
sample performance of the methods is examined through a Monte Carlo simulation
and the methods are used in an application to a job training experiment.
