  When studying treatment effects in multilevel studies, investigators commonly
use (semi-)parametric estimators, which make strong parametric assumptions
about the outcome, the treatment, and/or the correlation structure between
study units in a cluster. We propose a novel estimator of treatment effects
that does not make such assumptions. Specifically, the new estimator is shown
to be doubly robust, asymptotically Normal, and often more efficient than
existing estimators, all without having to make any parametric modeling
assumptions about the outcome, the treatment, and the correlation structure. We
achieve this by estimating two non-standard nuisance functions in causal
inference, the conditional propensity score and the outcome covariance model,
using existing existing machine learning methods designed for independent and
identically distributed (i.i.d) data. The new estimator is also demonstrated in
simulated and real data where the new estimator is drastically more efficient
than existing estimators, especially when studying cluster-specific treatment
effects.
