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.
The OSIRIS-REx Mission was selected under the NASA New Frontiers program and is scheduled for launch in September of 2016 for a rendezvous with, and collection of a sample from the surface of asteroid Bennu in 2019. 101955 Bennu (previously 1999 RQ 36 ) is an Apollo (near-Earth) asteroid originally discovered by the LINEAR project in 1999 which has since been classified as a potentially hazardous near-Earth object. The REgolith X-Ray Imaging Spectrometer (REXIS) was proposed jointly by MIT and Harvard and was subsequently accepted as a student led instrument for the determination of the elemental composition of the asteroid's surface as well as the surface distribution of select elements through solar induced X-ray fluorescence. REXIS consists of a detector plane that contains 4 X-ray CCDs integrated into a wide field coded aperture telescope with a focal length of 20 cm for the detection of regions with enhanced abundance in key elements at 50 m scales. Elemental surface distributions of approximately 50-200 m scales can be detected using the instrument as a simple collimator. An overview of the observation strategy of the REXIS instrument and expected performance are presented here.
The conditional average treatment effect (CATE) is the best point prediction of individual causal effects given individual baseline covariates and can help personalize treatments. However, as CATE only reflects the (conditional) average, it can wash out potential risks and tail events, which are crucially relevant to treatment choice. In aggregate analyses, this is usually addressed by measuring distributional treatment effect (DTE), such as differences in quantiles or tail expectations between treatment groups. Hypothetically, one can similarly fit covariate-conditional quantile regressions in each treatment group and take their difference, but this would not be robust to misspecification or provide agnostic best-in-class predictions. We provide a new robust and model-agnostic methodology for learning the conditional DTE (CDTE) for a wide class of problems that includes conditional quantile treatment effects, conditional superquantile treatment effects, and conditional treatment effects on coherent risk measures given by f -divergences. Our method is based on constructing a special pseudo-outcome and regressing it on baseline covariates using any given regression learner. Our method is model-agnostic in the sense that it can provide the best projection of CDTE onto the regression model class. Our method is robust in the sense that even if we learn these nuisances nonparametrically at very slow rates, we can still learn CDTEs at rates that depend on the class complexity and even conduct inferences on linear projections of CDTEs. We investigate the performance of our proposal in simulation studies, and we demonstrate its use in a case study of 401(k) eligibility effects on wealth.
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