| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w29242 |
| 来源ID | Working Paper 29242 |
| Semiparametric Estimation of Treatment Effects in Randomized Experiments | |
| Susan Athey; Peter J. Bickel; Aiyou Chen; Guido Imbens; Michael Pollmann | |
| 发表日期 | 2021-09-13 |
| 出版年 | 2021 |
| 语种 | 英语 |
| 摘要 | We develop new semiparametric methods for estimating treatment effects. We focus on a setting where the outcome distributions may be thick tailed, where treatment effects are small, where sample sizes are large and where assignment is completely random. This setting is of particular interest in recent experimentation in tech companies. We propose using parametric models for the treatment effects, as opposed to parametric models for the full outcome distributions. This leads to semiparametric models for the outcome distributions. We derive the semiparametric efficiency bound for this setting, and propose efficient estimators. In the case with a constant treatment effect one of the proposed estimators has an interesting interpretation as a weighted average of quantile treatment effects, with the weights proportional to (minus) the second derivative of the log of the density of the potential outcomes. Our analysis also results in an extension of Huber's model and trimmed mean to include asymmetry and a simplified condition on linear combinations of order statistics, which may be of independent interest. |
| 主题 | Econometrics ; Estimation Methods |
| URL | https://www.nber.org/papers/w29242 |
| 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/586916 |
| 推荐引用方式 GB/T 7714 | Susan Athey,Peter J. Bickel,Aiyou Chen,et al. Semiparametric Estimation of Treatment Effects in Randomized Experiments. 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 | ||
| w29242.pdf（714KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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