  We propose logit-based IV and augmented logit-based IV estimators that serve
as alternatives to the traditionally used 2SLS estimator in the model where
both the endogenous treatment variable and the corresponding instrument are
binary. Our novel estimators are as easy to compute as the 2SLS estimator but
have an advantage over the 2SLS estimator in terms of causal interpretability.
In particular, in certain cases where the probability limits of both our
estimators and the 2SLS estimator take the form of weighted-average treatment
effects, our estimators are guaranteed to yield non-negative weights whereas
the 2SLS estimator is not.
