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We study the problem of observational causal inference with continuoustreatments in the framework of inverse propensity-score weighting. To obtainstable weights, we design a new algorithm based on entropy balancing thatlearns weights to directly maximize causal inference accuracy using end-to-endoptimization. In the process of optimization, these weights are automaticallytuned to the specific dataset and causal inference algorithm being used. Weprovide a theoretical analysis demonstrating consistency of our approach. Usingsynthetic and real-world data, we show that our algorithm estimates causaleffect more accurately than baseline entropy balancing.

End-to-End Balancing for Causal Continuous Treatment-Effect Estimation