Shuyuan Chen, Peng Zhang, Yifan Cui
arXiv 4 Jan 2026 · Mathematics — Statistics Theory
arXiv:2601.01471 · PDF · DOI · OpenAlex · Extracted main text
Estimating causal effects of continuous treatments is a common problem in practice, for example, in studying 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 local identification of 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 dose-response function locally within the corresponding region. For estimation, we develop an augmented inverse probability weighting score for continuous treatments under a debiased machine learning framework with instrumental variables. We further establish the asymptotic properties when the 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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Edward H Kennedy, Zongming Ma, Matthew D McHugh, and Dylan S Small (2017) Non-parametric methods for doubly robust estimation of continuous treatment effects | 0.941 | 6 | 4 | 83% |
| 2 | Matteo Bonvini and Edward H Kennedy (2022) Fast convergence rates for dose-response estimation | 0.855 | 8 | 5 | 62% |
| 3 | Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo,… (2018) Double/debiased machine learning for treatment and structural parameters | 0.843 | 3 | 3 | 100% |
| 4 | Shuyuan Chen, Peng Zhang, and Yifan Cui (2025) Identification and debiased learning of causal effects with general instrumental variables self | 0.794 | 8 | 4 | 50% |
| 5 | Alexandre B. Tsybakov (2009) Introduction to Nonparametric Estimation, volume 161 of Springer Series in Statistics | 0.737 | 4 | 4 | 50% |
| 6 | Fernando Pires Hartwig, Linbo Wang, George Davey Smith, and Neil Mar… (2023) Average causal effect estimation via instrumental variables: the no simultaneous heterogeneity assumption | 0.644 | 2 | 2 | 100% |
| 7 | Chunrong Ai, Wei Huang, and Zheng Zhang (2026) Data-driven uniform inference for general continuous treatment models via minimum-variance weighting | 0.644 | 2 | 2 | 100% |
| 8 | Yifan Cui and Eric Tchetgen Tchetgen (2021) A semiparametric instrumental variable approach to optimal treatment regimes under endogeneity self | 0.644 | 2 | 2 | 100% |
| 9 | Haben Michael, Yifan Cui, Scott A Lorch, and Eric J Tchetgen Tchetgen (2024) Instrumental variable estimation of marginal structural mean models for time-varying treatment self | 0.644 | 2 | 2 | 100% |
| 10 | Kenta Takatsu and Ted Westling (2025) Debiased inference for a covariate-adjusted regression function | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 57 scored citations.