  Electroencephalogram (EEG) provides noninvasive measures of brain activity
and is found to be valuable for diagnosis of some chronic disorders.
Specifically, pre-treatment EEG signals in alpha and theta frequency bands have
demonstrated some association with anti-depressant response, which is
well-known to have low response rate. We aim to design an integrated pipeline
that improves the response rate of major depressive disorder patients by
developing an individualized treatment policy guided by the resting state
pre-treatment EEG recordings and other treatment effects modifiers. We first
design an innovative automatic site-specific EEG preprocessing pipeline to
extract features that possess stronger signals compared with raw data. We then
estimate the conditional average treatment effect using causal forests, and use
a doubly robust technique to improve the efficiency in the estimation of the
average treatment effect. We present evidence of heterogeneity in the treatment
effect and the modifying power of EEG features as well as a significant average
treatment effect, a result that cannot be obtained by conventional methods.
Finally, we employ an efficient policy learning algorithm to learn an optimal
depth-2 treatment assignment decision tree and compare its performance with
Q-Learning and outcome-weighted learning via simulation studies and an
application to a large multi-site, double-blind randomized controlled clinical
trial, EMBARC.
