论文标题
比较因果推理模型的评估框架
An evaluation framework for comparing causal inference models
论文作者
论文摘要
因果效应的估计是许多科学学科的核心目标。但是，这仍然是一项具有挑战性的任务，尤其是从观察数据估算效果时。最近，已经提出了一些有前途的机器学习模型来进行因果效应估计。对这些模型的评估是基于平均治疗效果（ATE）误差的平均值以及异质效应估计（PEHE）的精度。在本文中，我们建议使用具体统计证据（包括Dolan和Mor {é}的性能概况，以及非参数和事后统计测试）来补充因果推理模型的评估。这种方法背后的主要动机是消除了少数实例或模拟对基准测试过程的影响，在某些情况下，这占据了结果。我们使用拟议的评估方法比较了几种最新的因果效应估计模型。
Estimation of causal effects is the core objective of many scientific disciplines. However, it remains a challenging task, especially when the effects are estimated from observational data. Recently, several promising machine learning models have been proposed for causal effect estimation. The evaluation of these models has been based on the mean values of the error of the Average Treatment Effect (ATE) as well as of the Precision in Estimation of Heterogeneous Effect (PEHE). In this paper, we propose to complement the evaluation of causal inference models using concrete statistical evidence, including the performance profiles of Dolan and Mor{é}, as well as non-parametric and post-hoc statistical tests. The main motivation behind this approach is the elimination of the influence of a small number of instances or simulation on the benchmarking process, which in some cases dominate the results. We use the proposed evaluation methodology to compare several state-of-the-art causal effect estimation models.
