| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w28364 |
| 来源ID | Working Paper 28364 |
| Double-Robust Identification for Causal Panel Data Models | |
| Dmitry Arkhangelsky; Guido W. Imbens | |
| 发表日期 | 2021-01-25 |
| 出版年 | 2021 |
| 语种 | 英语 |
| 摘要 | We study identification and estimation of causal effects in settings with panel data. Traditionally researchers follow model-based identification strategies relying on assumptions governing the relation between the potential outcomes and the unobserved confounders. We focus on a novel, complementary, approach to identification where assumptions are made about the relation between the treatment assignment and the unobserved confounders. We introduce different sets of assumptions that follow the two paths to identification, and develop a double robust approach. We propose estimation methods that build on these identification strategies. |
| 主题 | Econometrics ; Estimation Methods |
| URL | https://www.nber.org/papers/w28364 |
| 来源智库 | National Bureau of Economic Research (United States) |
| 引用统计 | |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/586038 |
| 推荐引用方式 GB/T 7714 | Dmitry Arkhangelsky,Guido W. Imbens. Double-Robust Identification for Causal Panel Data Models. 2021. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 资源类型 | 版本类型 | 开放类型 | 使用许可 | ||
| w28364.pdf（521KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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