| 来源类型 | Working Paper |
| 规范类型 | 论文 |
| RCTs Against the Machine: Can Machine Learning Prediction Methods Recover Experimental Treatment Effects? | |
| Brian Prest; Casey Wichman; Karen Palmer | |
| 发表日期 | 2021-09-29 |
| 出版年 | 2021 |
| 语种 | 英语 |
| 摘要 | RFF researchers examine how well machine learning counterfactual prediction tools can estimate causal treatment effects. |
| 主题 | Policy Design and Evaluation,Electricity Markets |
| URL | https://www.rff.org/publications/working-papers/rcts-against-the-machine-can-machine-learning-prediction-methods-recover-experimental-treatment-effects/ |
| 来源智库 | Resources for the Future (United States) |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/588499 |
| 推荐引用方式 GB/T 7714 | Brian Prest,Casey Wichman,Karen Palmer. RCTs Against the Machine: Can Machine Learning Prediction Methods Recover Experimental Treatment Effects?. 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 | ||
| RCTs_Against_the_Mac（1626KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
| RCTs_Against_the_Mac（109KB） | 智库出版物 | 限制开放 | CC BY-NC-SA |  浏览 | ||
| 文件名： | RCTs_Against_the_Machine_Page_01.2e16d0ba.fill-620x800.png |
| 格式： | image/png |
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