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.

翻译：我们研究持续处理的观察因果推断问题。我们侧重于估算不经常观察的治疗值的因果反应曲线的挑战。我们设计了一种新的算法,其基础是酶平衡框架,通过端到端优化学习能够直接最大限度地实现因果推断准确性的权重。我们的权重可以针对不同的数据集和因果推断算法进行定制。我们为连续处理的酶平衡的一致性提出了一个新理论。我们使用合成和真实世界的数据,我们表明我们提议的算法在因果推断准确性方面优于酶平衡。