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Specifically, pre\u2010treatment EEG signals in the alpha and theta frequency bands have demonstrated some association with antidepressant response, which is well\u2010known to have a low response rate. We aim to design an integrated pipeline that improves the response rate of patients with major depressive disorder by developing a treatment policy guided by the resting state pre\u2010treatment EEG recordings and other treatment effects modifiers. First, we design an innovative automatic site\u2010specific EEG preprocessing pipeline to extract features with stronger signals than raw data. We then estimate the conditional average treatment effect (CATE) using causal forests and use a doubly robust technique to improve efficiency in the estimation of the average treatment effect. 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