A neural augmented Naive Bayes layer is proposed to rank subjects for treatments that combine continuous intensity and discrete assignment, but the causal estimator rests on an unjustified weighting identity.
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Deep Learning of Continuous and Structured Policies for Aggregated Heterogeneous Treatment Effects
A neural augmented Naive Bayes layer is proposed to rank subjects for treatments that combine continuous intensity and discrete assignment, but the causal estimator rests on an unjustified weighting identity.