论文标题
异构治疗效果的校准误差
Calibration Error for Heterogeneous Treatment Effects
论文作者
论文摘要
最近，许多研究人员采用了用于建模异质治疗效果（HTES）的高级数据驱动方法。即便如此，HTES的估计还是一项艰巨的任务 - 这些方法经常过度或低估治疗效果，从而导致所得模型的校准不佳。但是，尽管存在许多用于评估预测和分类模型校准的方法，但评估HTE模型校准的正式方法仅限于校准斜率。在本文中，我们定义了\ smash {（$ \ ell_2 $）}的类似物的HTES预期校准错误，并提出了一个可靠的估计器。我们的方法是由双重强大的治疗效果估计器激励的，使其无偏见，并且有弹性，并具有混淆，过度拟合和高维问题。此外，我们的方法很简单地适应许多结构，在这些结构下可以鉴定出治疗效果，包括随机试验，观察性研究和生存分析。我们说明了如何使用我们提出的指标通过应用于Criteo-Oplift试验来评估学习HTE模型的校准。
Recently, many researchers have advanced data-driven methods for modeling heterogeneous treatment effects (HTEs). Even still, estimation of HTEs is a difficult task -- these methods frequently over- or under-estimate the treatment effects, leading to poor calibration of the resulting models. However, while many methods exist for evaluating the calibration of prediction and classification models, formal approaches to assess the calibration of HTE models are limited to the calibration slope. In this paper, we define an analogue of the \smash{($\ell_2$)} expected calibration error for HTEs, and propose a robust estimator. Our approach is motivated by doubly robust treatment effect estimators, making it unbiased, and resilient to confounding, overfitting, and high-dimensionality issues. Furthermore, our method is straightforward to adapt to many structures under which treatment effects can be identified, including randomized trials, observational studies, and survival analysis. We illustrate how to use our proposed metric to evaluate the calibration of learned HTE models through the application to the CRITEO-UPLIFT Trial.
