Estimation of Local Average Treatment Effect by Data Combination

Kazuhiko Shinoda, Takahiro Hoshino

研究成果: Conference contribution


It is important to estimate the local average treatment effect (LATE) when compliance with a treatment assignment is incomplete. The previously proposed methods for LATE estimation required all relevant variables to be jointly observed in a single dataset; however, it is sometimes difficult or even impossible to collect such data in many real-world problems for technical or privacy reasons. We consider a novel problem setting in which LATE, as a function of covariates, is nonparametrically identified from the combination of separately observed datasets. For estimation, we show that the direct least squares method, which was originally developed for estimating the average treatment effect under complete compliance, is applicable to our setting. However, model selection and hyperparameter tuning for the direct least squares estimator can be unstable in practice since it is defined as a solution to the minimax problem. We then propose a weighted least squares estimator that enables simpler model selection by avoiding the minimax objective formulation. Unlike the inverse probability weighted (IPW) estimator, the proposed estimator directly uses the pre-estimated weight without inversion, avoiding the problems caused by the IPW methods. We demonstrate the effectiveness of our method through experiments using synthetic and real-world datasets.

ホスト出版物のタイトルAAAI-22 Technical Tracks 8
出版社Association for the Advancement of Artificial Intelligence
ISBN(電子版)1577358767, 9781577358763
出版ステータスPublished - 2022 6月 30
イベント36th AAAI Conference on Artificial Intelligence, AAAI 2022 - Virtual, Online
継続期間: 2022 2月 222022 3月 1


名前Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022


Conference36th AAAI Conference on Artificial Intelligence, AAAI 2022
CityVirtual, Online

ASJC Scopus subject areas

  • 人工知能


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