Meta-learning for heterogeneous treatment effect estimation with closed-form solvers

• URL: http://arxiv.org/abs/2305.11353v1
• Date: Fri, 19 May 2023 00:07:38 GMT
• Title: Meta-learning for heterogeneous treatment effect estimation with closed-form solvers
• Authors: Tomoharu Iwata, Yoichi Chikahara
• Abstract summary: 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.
• Score: 30.343569752920754
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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