mlsanityData to 14 Oct 2026

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

arXiv 2305.11353 ↗First posted 19 May 2023stat.ML cs.AI cs.LGcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 26 of its 46 references that are in the map. First posted on arXiv 19 May 2023.

Builds on

26 of 46 references.

References · 26

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14 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Foundation Models for Causal Inference via Prior-Data Fitted Networkscited by 32
  2. 2026Moment Matters: Mean and Variance Causal Graph Discovery from Heteroscedastic Observational Datacited by 0