  When evaluating the effectiveness of a treatment, policy, or intervention,
the desired measure of effectiveness may be expensive to collect, not routinely
available, or may take a long time to occur. In these cases, it is sometimes
possible to identify a surrogate outcome that can more easily/quickly/cheaply
capture the effect of interest. Theory and methods for evaluating the strength
of surrogate markers have been well studied in the context of a single
surrogate marker measured in the course of a randomized clinical study.
However, methods are lacking for quantifying the utility of surrogate markers
when the dimension of the surrogate grows and/or when study data are
observational. We propose an efficient nonparametric method for evaluating
high-dimensional surrogate markers in studies where the treatment need not be
randomized. Our approach draws on a connection between quantifying the utility
of a surrogate marker and the most fundamental tools of causal inference --
namely, methods for estimating the average treatment effect. We show that
recently developed methods for incorporating machine learning methods into the
estimation of average treatment effects can be used for evaluating surrogate
markers. This allows us to derive limiting asymptotic distributions for key
quantities, and we demonstrate their good performance in simulation.
