We consider the estimation of heterogeneous treatment effects with arbitrary
machine learning methods in the presence of unobserved confounders with the aid
of a valid instrument. Such settings arise in A/B tests with an intent-to-treat
structure, where the experimenter randomizes over which user will receive a
recommendation to take an action, and we are interested in the effect of the
downstream action. We develop a statistical learning approach to the estimation
of heterogeneous effects, reducing the problem to the minimization of an
appropriate loss function that depends on a set of auxiliary models (each
corresponding to a separate prediction task). The reduction enables the use of
all recent algorithmic advances (e.g. neural nets, forests). We show that the
estimated effect model is robust to estimation errors in the auxiliary models,
by showing that the loss satisfies a Neyman orthogonality criterion. Our
approach can be used to estimate projections of the true effect model on
simpler hypothesis spaces. When these spaces are parametric, then the parameter
estimates are asymptotically normal, which enables construction of confidence
sets. We applied our method to estimate the effect of membership on downstream
webpage engagement on TripAdvisor, using as an instrument an intent-to-treat
A/B test among 4 million TripAdvisor users, where some users received an easier
membership sign-up process. We also validate our method on synthetic data and
on public datasets for the effects of schooling on income.

本研究使用有效的工具变量来考虑具有任意机器学习方法的异质性处理效应估计，并减少了问题到最小化一个适当的损失函数（依赖于一组辅助模型，每个模型对应一个独立的预测任务）的规模。