论文标题
比较估计多价处理异质效应的元学习者
Comparison of meta-learners for estimating multi-valued treatment heterogeneous effects
论文作者
论文摘要
有条件的平均治疗效果（CATE）估计是与观察数据有关的因果推断的主要挑战之一。除了基于机器学习的模型外，还开发了称为元学习者的非参数估计器，以估算CATE，其主要优势是不将估计限制为特定的监督学习方法。但是，当治疗不是二进制时，随着幼稚扩展的某些局限性出现时，此任务变得更加复杂。本文研究了估计多价处理的异质效应的元学习者。我们考虑不同的元学习者，并将其误差上限进行理论分析作为重要参数的功能，例如治疗水平的数量，表明幼稚的扩展并不总是提供令人满意的结果。我们介绍和讨论随着治疗次数增加而表现良好的元学习者。我们从经验上证实了通过合成和半合成数据集的这些方法的优势和劣势。
Conditional Average Treatment Effects (CATE) estimation is one of the main challenges in causal inference with observational data. In addition to Machine Learning based-models, nonparametric estimators called meta-learners have been developed to estimate the CATE with the main advantage of not restraining the estimation to a specific supervised learning method. This task becomes, however, more complicated when the treatment is not binary as some limitations of the naive extensions emerge. This paper looks into meta-learners for estimating the heterogeneous effects of multi-valued treatments. We consider different meta-learners, and we carry out a theoretical analysis of their error upper bounds as functions of important parameters such as the number of treatment levels, showing that the naive extensions do not always provide satisfactory results. We introduce and discuss meta-learners that perform well as the number of treatments increases. We empirically confirm the strengths and weaknesses of those methods with synthetic and semi-synthetic datasets.
