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

Mohammad Taha Bahadori · Eric Tchetgen Tchetgen · David Heckerman

Hall G
[ Abstract ] [ Livestream: Visit MISC: Representation Learning/Causality ]
Wed 20 Jul 10:30 a.m. — 10:35 a.m. PDT
[ Slides [ Paper PDF

We study the problem of observational causal inference with continuous treatment. We focus on the challenge of estimating the causal response curve for infrequently-observed treatment values.We design a new algorithm based on the framework of entropy balancing which learns weights that directly maximize causal inference accuracy using end-to-end optimization. Our weights can be customized for different datasets and causal inference algorithms. We propose a new theory for consistency of entropy balancing for continuous treatments. Using synthetic and real-world data, we show that our proposed algorithm outperforms the entropy balancing in terms of causal inference accuracy.

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