| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w26562 |
| 来源ID | Working Paper 26562 |
| Factorial Designs, Model Selection, and (Incorrect) Inference in Randomized Experiments | |
| Karthik Muralidharan; Mauricio Romero; Kaspar Wüthrich | |
| 发表日期 | 2019-12-16 |
| 出版年 | 2019 |
| 语种 | 英语 |
| 摘要 | Factorial designs are widely used for studying multiple treatments in one experiment. While t-tests based on the “long” model (including main and interaction effects) provide valid inferences against “business-as-usual” counterfactuals, “short” model t-tests (that ignore interactions) yield higher power if the interactions are zero, but incorrect inferences otherwise. Out of 27 factorial experiments published in top-5 journals in 2007–2017, 19 use the short model. We reanalyze these experiments, and show that over half of their published results lose significance when interactions are included. We show that testing the interactions using the long model and presenting the short model if the interactions are not significantly different from zero leads to incorrect inference due to the implied data-dependent model selection. Based on recent econometric advances, we show that local power improvements over the long model are possible. However, if the main effects are of primary interest, leaving the interaction cells empty yields valid inferences and global power improvements. In addition, the sample size needed to detect interactions is substantially larger than that required to detect main effects, resulting in most experiments being under-powered to detect interactions. Thus, using factorial designs to explore whether interactions are meaningful can be problematic because interaction estimates are likely to considerably overestimate the magnitude of the true effect conditional on being significant. |
| 主题 | Econometrics ; Estimation Methods ; Experimental Design |
| URL | https://www.nber.org/papers/w26562 |
| 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/584236 |
| 推荐引用方式 GB/T 7714 | Karthik Muralidharan,Mauricio Romero,Kaspar Wüthrich. Factorial Designs, Model Selection, and (Incorrect) Inference in Randomized Experiments. 2019. |
| 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 | ||
| w26562.pdf（598KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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