Shuyuan Chen, Peng Zhang, Yifan Cui
arXiv 23 Oct 2025 · Statistics — Methodology
arXiv:2510.20404 · PDF · DOI · OpenAlex · Extracted main text
Instrumental variable methods are fundamental to causal inference when treatment assignment is confounded by unobserved variables. In this article, we develop a general nonparametric causal framework for identification and learning with multi-categorical or continuous instrumental variables. Specifically, the mean potential outcomes and the average treatment effect can be identified via a regular weighting function derived from the proposed framework. Leveraging semiparametric theory, we derive efficient influence functions and construct two consistent, asymptotically normal estimators via debiased machine learning. The first estimator uses a prespecified weighting function, while the second estimator selects the optimal weighting function adaptively. 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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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 | Miguel A. Hernán and James M. Robins (2020) Causal Inference: What If | 0.843 | 3 | 3 | 100% |
| 2 | Linbo Wang and Eric Tchetgen Tchetgen (2018) Bounded, efficient and multiply robust estimation of average treatment effects using instrumental variables | 0.737 | 3 | 3 | 67% |
| 3 | Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo,… (2018) Double/debiased machine learning for treatment and structural parameters | 0.644 | 2 | 2 | 100% |
| 4 | Yifan Cui and Eric Tchetgen Tchetgen (2021) A semiparametric instrumental variable approach to optimal treatment regimes under endogeneity self | 0.644 | 2 | 2 | 100% |
| 5 | Lars Peter Hansen (1982) Large sample properties of generalized method of moments estimators | 0.644 | 2 | 2 | 100% |
| 6 | Whitney K Newey and James L Powell (2003) Instrumental variable estimation of nonparametric models | 0.644 | 2 | 2 | 100% |
| 7 | Eric J Tchetgen Tchetgen, Andrew Ying, Yifan Cui, Xu Shi, and Wang M… (2024) An introduction to proximal causal inference self | 0.644 | 2 | 2 | 100% |
| 8 | Sukjin Han (2024) Optimal dynamic treatment regimes and partial welfare ordering | 0.585 | 3 | 1 | 100% |
| 9 | Jiewen Liu, Chan Park, Yonghoon Lee, Yunshu Zhang, Mengxin Yu, James… (2025) The multiplicative instrumental variable model | 0.511 | 3 | 2 | 33% |
| 10 | Yifan Cui, Haben Michael, Frank Tanser, and Eric Tchetgen Tchetgen (2023) Instrumental variable estimation of the marginal structural cox model for time-varying treatments self | 0.511 | 2 | 2 | 50% |
Showing the top 10 of 62 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Double Machine Learning of Continuous Treatment Effects with General Instrumental Variables | 0.794 | 8 | 4 |