Estimates treatment effects with machine learning using instruments in A/B tests.
problem Estimating heterogeneous treatment effects with unobserved confounders in A/B tests.
method Develops a statistical learning approach using machine learning methods and auxiliary models.
result Shows robustness of estimated effect model to auxiliary model errors and provides asymptotic normality for parameter estimates.