Identification and Debiased Learning of Causal Effects with General Instrumental Variables

• URL: http://arxiv.org/abs/2510.20404v1
• Date: Thu, 23 Oct 2025 10:10:11 GMT
• Title: Identification and Debiased Learning of Causal Effects with General Instrumental Variables
• Authors: Shuyuan Chen, Peng Zhang, Yifan Cui,
• Abstract summary: We develop a general nonparametric framework for identification and learning with multi-categorical or continuous instrumental variables.<n>We derive consistent, efficientally normal estimators via debiased machine learning.<n>We demonstrate the proposed method by employing simulation studies and analyzing real data from the Job Training Partnership Act program.
• Score: 5.00731378650601
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Instrumental variable methods are fundamental to causal inference when treatment assignment is confounded by unobserved variables. In this article, we develop a general nonparametric framework for identification and learning with multi-categorical or continuous instrumental variables. Specifically, we propose an additive instrumental variable framework to identify mean potential outcomes and the average treatment effect with a weighting function. Leveraging semiparametric theory, we derive efficient influence functions and construct consistent, asymptotically normal estimators via debiased machine learning. Extensions to longitudinal data, dynamic treatment regimes, and multiplicative instrumental variables are further developed. We demonstrate the proposed method by employing simulation studies and analyzing real data from the Job Training Partnership Act program.

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