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

Mohammad Taha Bahadori; Eric Tchetgen Tchetgen; David E. Heckerman

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arXiv · 2107.13068

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

Mohammad Taha Bahadori·Eric Tchetgen Tchetgen·David E. Heckerman

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Abstract

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.

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Mohammad Taha Bahadori, Eric Tchetgen Tchetgen, David E. Heckerman. 2021-07-27. End-to-End Balancing for Causal Continuous Treatment-Effect Estimation. https://arxiv.org/abs/2107.13068

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