| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w30302 |
| 来源ID | Working Paper 30302 |
| Long Story Short: Omitted Variable Bias in Causal Machine Learning | |
| Victor Chernozhukov; Carlos Cinelli; Whitney Newey; Amit Sharma; Vasilis Syrgkanis | |
| 发表日期 | 2022-08-01 |
| 出版年 | 2022 |
| 语种 | 英语 |
| 摘要 | We derive general, yet simple, sharp bounds on the size of the omitted variable bias for a broad class of causal parameters that can be identified as linear functionals of the conditional expectation function of the outcome. Such functionals encompass many of the traditional targets of investigation in causal inference studies, such as, for example, (weighted) average of potential outcomes, average treatment effects (including subgroup effects, such as the effect on the treated), (weighted) average derivatives, and policy effects from shifts in covariate distribution -- all for general, nonparametric causal models. Our construction relies on the Riesz-Frechet representation of the target functional. Specifically, we show how the bound on the bias depends only on the additional variation that the latent variables create both in the outcome and in the Riesz representer for the parameter of interest. Moreover, in many important cases (e.g, average treatment effects and avearage derivatives) the bound is shown to depend on easily interpretable quantities that measure the explanatory power of the omitted variables. Therefore, simple plausibility judgments on the maximum explanatory power of omitted variables (in explaining treatment and outcome variation) are sufficient to place overall bounds on the size of the bias. Furthermore, we use debiased machine learning to provide flexible and efficient statistical inference on learnable components of the bounds. Finally, empirical examples demonstrate the usefulness of the approach. |
| 主题 | Econometrics ; Estimation Methods |
| URL | https://www.nber.org/papers/w30302 |
| 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/587975 |
| 推荐引用方式 GB/T 7714 | Victor Chernozhukov,Carlos Cinelli,Whitney Newey,et al. Long Story Short: Omitted Variable Bias in Causal Machine Learning. 2022. |
| 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 | ||
| w30302.pdf（560KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。
