Generative Learner for Distributional Causal Effects
The proposed generative learner reduces out-of-sample mean squared error relative to the generalized random forest, double machine learning, and generative adversarial networks, with gains ranging from 5.4% to 93.5% on average across experimental designs.
Maria Nareklishvili, Nicholas G. Polson, Vadim Sokolov
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