Meta-learning for heterogeneous treatment effect estimation with
  closed-form solvers
Tomoharu Iwata Yoichi Chikahara
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.