This article proposes a meta-learning method for estimating the conditional average treatment effect (CATE) from a few observational data. The proposed method learns how to estimate CATEs from multiple tasks and uses the knowledge for unseen tasks. In the proposed method, based on the meta-learner framework, we decompose the CATE estimation problem into sub-problems. For each sub-problem, we formulate our estimation models using neural networks with task-shared and task-specific parameters. With our formulation, we can obtain optimal task-specific parameters in a closed form that are differentiable with respect to task-shared parameters, making it possible to perform effective meta-learning. The task-shared parameters are trained such that the expected CATE estimation performance in few-shot settings is improved by minimizing the difference between a CATE estimated with a large amount of data and one estimated with just a few data. Our experimental results demonstrate that our method outperforms the existing meta-learning approaches and CATE estimation methods.

翻译：本文提出了一种元学习方法，用于从少量观测数据中估计条件平均处理效应（CATE）。该方法通过多任务学习CATE的估计方式，并将学到的知识应用于未见任务。在提出的方法中，基于元学习框架将CATE估计问题分解为多个子问题。针对每个子问题，我们采用包含任务共享参数与任务特定参数的神经网络构建估计模型。通过这种建模方式，我们能够以闭式形式获得最优的任务特定参数，且这些参数对任务共享参数具有可微性，从而支持高效的元学习训练。任务共享参数的训练目标是通过最小化基于大量数据估计的CATE与仅用少量数据估计的CATE之间的差异，提升少样本场景下的期望CATE估计性能。实验结果表明，我们的方法优于现有元学习方法和CATE估计方法。