Generalized Stable Weights via Neural Gibbs Density
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We present a generalized balancing weight method fully available for estimating causal effects for an arbitrary mixture of discrete and continuous interventions. Our weights are trainable through back-propagation, and we give a method for estimating the weights via neural network algorithms. In addition, we also provide a method to measure the performance of our weights by estimating the mutual information for the balanced distribution. Our method is easy to implement with any present deep learning libraries, and the weights from it can be used in most state-of-art supervised algorithms.
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