Before revision:  Our method is based on a sequential regression peeling process, which we show can be equivalently interpreted as a Neyman orthogonal moment estimator. This allows us to show root-n asymptotic normality of the estimated causal effects .
After revision:  Our method is based on a sequential regression peeling process, typical in g-estimation, and formulates a strongly convex objective at each stage, which allows us to show root-n asymptotic normality of the estimated causal effects .