Meta-learning for heterogeneous treatment effect estimation with closed-form solvers
This addresses the challenge of heterogeneous treatment effect estimation in few-shot settings, which is important for fields like healthcare and policy-making, but it is incremental as it builds on existing meta-learner frameworks.
The paper tackles the problem of estimating conditional average treatment effects (CATE) from limited observational data by proposing a meta-learning method that learns from multiple tasks and applies this knowledge to unseen tasks, with experimental results showing it outperforms existing approaches.
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.