Inference on Nonlinear Counterfactual Functionals under a Multiplicative IV Model
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866914012623536128 |
|---|---|
| author | Lee, Yonghoon Yu, Mengxin Liu, Jiewen Park, Chan Zhang, Yunshu Robins, James M. Tchetgen, Eric J. Tchetgen |
| author_facet | Lee, Yonghoon Yu, Mengxin Liu, Jiewen Park, Chan Zhang, Yunshu Robins, James M. Tchetgen, Eric J. Tchetgen |
| contents | Instrumental variable (IV) methods play a central role in causal inference, particularly in settings where treatment assignment is confounded by unobserved variables. IV methods have been extensively developed in recent years and applied across diverse domains, from economics to epidemiology. In this work, we study the recently introduced multiplicative IV (MIV) model and demonstrate its utility for causal inference beyond the average treatment effect. In particular, we show that it enables identification and inference for a broad class of counterfactual functionals characterized by moment equations. This includes, for example, inference on quantile treatment effects. We develop methods for efficient and multiply robust estimation of such functionals, and provide inference procedures with asymptotic validity. Experimental results demonstrate that the proposed procedure performs well even with moderate sample sizes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_15612 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Inference on Nonlinear Counterfactual Functionals under a Multiplicative IV Model Lee, Yonghoon Yu, Mengxin Liu, Jiewen Park, Chan Zhang, Yunshu Robins, James M. Tchetgen, Eric J. Tchetgen Methodology Instrumental variable (IV) methods play a central role in causal inference, particularly in settings where treatment assignment is confounded by unobserved variables. IV methods have been extensively developed in recent years and applied across diverse domains, from economics to epidemiology. In this work, we study the recently introduced multiplicative IV (MIV) model and demonstrate its utility for causal inference beyond the average treatment effect. In particular, we show that it enables identification and inference for a broad class of counterfactual functionals characterized by moment equations. This includes, for example, inference on quantile treatment effects. We develop methods for efficient and multiply robust estimation of such functionals, and provide inference procedures with asymptotic validity. Experimental results demonstrate that the proposed procedure performs well even with moderate sample sizes. |
| title | Inference on Nonlinear Counterfactual Functionals under a Multiplicative IV Model |
| topic | Methodology |
| url | https://arxiv.org/abs/2507.15612 |