  We study the problem of observational causal inference with continuous
treatments in the framework of inverse propensity-score weighting. To obtain
stable weights, we design a new algorithm based on entropy balancing that
learns weights to directly maximize causal inference accuracy using end-to-end
optimization. In the process of optimization, these weights are automatically
tuned to the specific dataset and causal inference algorithm being used. We
provide a theoretical analysis demonstrating consistency of our approach. Using
synthetic and real-world data, we show that our algorithm estimates causal
effect more accurately than baseline entropy balancing.
