  I propose a locally robust semiparametric framework for estimating causal
effects using the popular examiner IV design, in the presence of many examiners
and possibly many covariates relative to the sample size. The key ingredient of
this approach is an orthogonal moment function that is robust to biases and
local misspecification from the first step estimation of the examiner IV. I
derive the orthogonal moment function and show that it delivers multiple
robustness where the outcome model or at least one of the first step components
is misspecified but the estimating equation remains valid. The proposed
framework not only allows for estimation of the examiner IV in the presence of
many examiners and many covariates relative to sample size, using a wide range
of nonparametric and machine learning techniques including LASSO, Dantzig,
neural networks and random forests, but also delivers root-n consistent
estimation of the parameter of interest under mild assumptions.
