  Endogeneity and missing data are common issues in empirical research. We
investigate how both jointly affect inference on causal parameters.
Conventional methods to estimate the variance, which treat the imputed data as
if it was observed in the first place, are not reliable. We derive the
asymptotic variance and propose a heteroskedasticity robust variance estimator
for two-stage least squares which accounts for the imputation. Monte Carlo
simulations support our theoretical findings.
