  We propose an approach to better inform treatment decisions at an individual
level by adapting recent advances in average treatment effect estimation to
conditional average treatment effect estimation. Our work is based on doubly
robust estimation methods, which combine flexible machine learning tools to
produce efficient effect estimates while relaxing parametric assumptions about
the data generating process. Refinements to doubly robust methods have achieved
faster convergence by incorporating 3-way cross-fitting, which entails dividing
the sample into three partitions, using the first to estimate the conditional
probability of treatment, the second to estimate the conditional expectation of
the outcome, and the third to perform a first order bias correction step. Here,
we combine the approaches of 3-way cross-fitting and pseudo-outcome regression
to produce personalized effect estimates. We show that this approach yields
fast convergence rates under a smoothness condition on the conditional
expectation of the outcome.
