Identification and Estimation of Conditional Average Partial Causal Effects via Instrumental Variable
Fuente:
arXiv
Guardado en:
| Autores principales: | , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866911895151181824 |
|---|---|
| author | Kawakami, Yuta Kuroki, Manabu Tian, Jin |
| author_facet | Kawakami, Yuta Kuroki, Manabu Tian, Jin |
| contents | There has been considerable recent interest in estimating heterogeneous causal effects. In this paper, we study conditional average partial causal effects (CAPCE) to reveal the heterogeneity of causal effects with continuous treatment. We provide conditions for identifying CAPCE in an instrumental variable setting. Notably, CAPCE is identifiable under a weaker assumption than required by a commonly used measure for estimating heterogeneous causal effects of continuous treatment. We develop three families of CAPCE estimators: sieve, parametric, and reproducing kernel Hilbert space (RKHS)-based, and analyze their statistical properties. We illustrate the proposed CAPCE estimators on synthetic and real-world data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_11130 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Identification and Estimation of Conditional Average Partial Causal Effects via Instrumental Variable Kawakami, Yuta Kuroki, Manabu Tian, Jin Machine Learning There has been considerable recent interest in estimating heterogeneous causal effects. In this paper, we study conditional average partial causal effects (CAPCE) to reveal the heterogeneity of causal effects with continuous treatment. We provide conditions for identifying CAPCE in an instrumental variable setting. Notably, CAPCE is identifiable under a weaker assumption than required by a commonly used measure for estimating heterogeneous causal effects of continuous treatment. We develop three families of CAPCE estimators: sieve, parametric, and reproducing kernel Hilbert space (RKHS)-based, and analyze their statistical properties. We illustrate the proposed CAPCE estimators on synthetic and real-world data. |
| title | Identification and Estimation of Conditional Average Partial Causal Effects via Instrumental Variable |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2401.11130 |