| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w24678 |
| 来源ID | Working Paper 24678 |
| Generic Machine Learning Inference on Heterogeneous Treatment Effects in Randomized Experiments, with an Application to Immunization in India | |
| Victor Chernozhukov; Mert Demirer; Esther Duflo; Iván Fernández-Val | |
| 发表日期 | 2018-06-11 |
| 出版年 | 2018 |
| 语种 | 英语 |
| 摘要 | We propose strategies to estimate and make inference on key features of heterogeneous effects in randomized experiments. These key features include best linear predictors of the effects on machine learning proxies, average effects sorted by impact groups, and average characteristics of most and least impacted units. The approach is valid in high dimensional settings, where the effects are proxied by machine learning methods. We post-process these proxies into the estimates of the key features. Our approach is generic, it can be used in conjunction with penalized methods, deep and shallowneural networks, canonical and newrandom forests, boosted trees, and ensemble methods. Estimation and inference are based on repeated data splitting to avoid overfitting and achieve validity. For inference, we take medians of p-values and medians of confidence intervals, resulting from many different data splits, and then adjust their nominal level to guarantee uniform validity. This variational inference method, which quantifies the uncertainty coming from both parameter estimation and data splitting, is shown to be uniformly valid for a large class of data generating processes. We illustrate the use of the approach with a randomized field experiment that evaluated a combination of nudges to stimulate demand for immunization in India. |
| 主题 | Econometrics ; Estimation Methods ; Microeconomics ; Households and Firms ; Financial Economics ; Financial Institutions ; Development and Growth ; Development |
| URL | https://www.nber.org/papers/w24678 |
| 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/582351 |
| 推荐引用方式 GB/T 7714 | Victor Chernozhukov,Mert Demirer,Esther Duflo,et al. Generic Machine Learning Inference on Heterogeneous Treatment Effects in Randomized Experiments, with an Application to Immunization in India. 2018. |
| 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 | ||
| w24678.pdf（633KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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