{"ID":2822857,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2601.01471","arxiv_id":"2601.01471","title":"Double Machine Learning of Continuous Treatment Effects with General Instrumental Variables","abstract":"Estimating causal effects of continuous treatments is a common problem in practice, for example, in studying average dose-response functions. Classical analyses typically assume that all confounders are fully observed, whereas in real-world applications, unmeasured confounding often persists. In this article, we propose a novel framework for the identification of average dose-response functions using instrumental variables, thereby mitigating bias induced by unobserved confounders. We introduce the concept of a uniform regular weighting function and consider covering the treatment space with a finite collection of open sets. On each of these sets, such a weighting function exists, allowing us to identify the average dose-response function locally within the corresponding region. For estimation, we propose an augmented inverse probability weighted score for continuous treatments with instrumental variables under a debiased machine learning framework, and provide practical guidance to adaptively establish regular weighting functions from the data. We further establish the asymptotic properties when the average dose-response function is estimated via kernel regression or empirical risk minimization. Finally, we conduct both simulation and empirical studies to assess the finite-sample performance of the proposed methods.","short_abstract":"Estimating causal effects of continuous treatments is a common problem in practice, for example, in studying average dose-response functions. Classical analyses typically assume that all confounders are fully observed, whereas in real-world applications, unmeasured confounding often persists. In this article, we propos...","url_abs":"https://arxiv.org/abs/2601.01471","url_pdf":"https://arxiv.org/pdf/2601.01471v2","authors":"[\"Shuyuan Chen\",\"Peng Zhang\",\"Yifan Cui\"]","published":"2026-01-04T10:29:53Z","proceeding":"math.ST","tasks":"[\"math.ST\",\"econ.EM\",\"stat.ME\",\"stat.ML\"]","methods":"[]","has_code":false}