We address the Individualized continuous treatment effect (ICTE) estimation problem where we predict the effect of any continuous-valued treatment on an individual using observational data. The main challenge in this estimation task is the potential confounding of treatment assignment with an individual's covariates in the training data, whereas during inference ICTE requires prediction on independently sampled treatments. In contrast to prior work that relied on regularizers or unstable GAN training, we advocate the direct approach of augmenting training individuals with independently sampled treatments and inferred counterfactual outcomes. We infer counterfactual outcomes using a two-pronged strategy: a Gradient Interpolation for close-to-observed treatments, and a Gaussian Process based Kernel Smoothing which allows us to downweigh high variance inferences. We evaluate our method on five benchmarks and show that our method outperforms six state-of-the-art methods on the counterfactual estimation error. We analyze the superior performance of our method by showing that (1) our inferred counterfactual responses are more accurate, and (2) adding them to the training data reduces the distributional distance between the confounded training distribution and test distribution where treatment is independent of covariates. Our proposed method is model-agnostic and we show that it improves ICTE accuracy of several existing models.

翻译：我们针对个体化连续处理效应（ICTE）估计问题展开研究，旨在利用观测数据预测任意连续值处理对个体的影响。该估计任务的主要挑战在于训练数据中处理分配与个体协变量间存在潜在混淆，而ICTE推断要求对独立采样的处理进行预测。与以往依赖正则化器或不稳定GAN训练的方法不同，我们主张采用直接方法：通过为训练个体补充独立采样的处理及其推断的反事实结果来增强数据。我们采用双管齐下的策略推断反事实结果：对于接近观测处理的样本使用梯度插值法，并采用基于高斯过程的核平滑方法以降低高方差推断的权重。我们在五个基准上评估了该方法，结果表明在反事实估计误差方面，我们的方法优于六种最先进方法。我们通过以下两点分析了本方法的优越性能：（1）我们推断的反事实响应更为准确；（2）将这些响应加入训练数据后，可降低混淆训练分布与处理独立于协变量的测试分布之间的分布距离。我们提出的方法与模型无关，且能显著提升多种现有模型的ICTE估计精度。