Fisher-Schultz Lecture: Generic Machine Learning Inference on Heterogenous Treatment Effects in Randomized Experiments, with an Application to Immunization in India
Fisher–Schultz 讲座：随机实验中异质性处理效应的通用机器学习推断及印度免疫接种应用AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
本页为预印本版本；arXiv 收录、作者填写的期刊信息以及作者既有发表经历，都不单独证明本篇已接收。作者发表记录按 OpenAlex 身份与完整姓名、本站覆盖期刊 ISSN 及正式卷期核对，只展示已核实例，档案仍可能误合并。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要直接研究随机实验的异质性处理效应推断，并以免疫接种随机现场实验展示方法。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
Fisher–Schultz 讲座：随机实验中异质性处理效应的通用机器学习推断及印度免疫接种应用
- 研究问题
- 在高维随机实验中，如何利用可能不一致的机器学习效应代理，对异质性效应的线性预测、影响分组及群体特征作推断？
- 方法与思路
- 作者对机器学习代理进行后处理，以重复数据划分避免过拟合，再对多次划分的 p 值和置信区间进行分位数聚合。
- 材料陈述的贡献
- 摘要称建立关键异质性特征与分位数聚合的推断性质，降低相对单次划分的估计风险，并提出改善代理的因果学习目标。
- 阅读关注点
- 应核对各异质性目标的定义、代理有效性所需条件、重复划分与区间聚合的规则，以及因果学习目标的理论保证。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收未核实；摘要未列出完整条件，不能据此声称个体处理效应被一致估计。
原文摘要
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 using 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 (but not necessarily consistently estimated) by predictive and causal machine learning methods. We post-process these proxies into estimates of the key features. Our approach is generic, it can be used in conjunction with penalized methods, neural networks, random forests, boosted trees, and ensemble methods, both predictive and causal. Estimation and inference are based on repeated data splitting to avoid overfitting and achieve validity. We use quantile aggregation of the results across many potential splits, in particular taking medians of p-values and medians and other quantiles of confidence intervals. We show that quantile aggregation lowers estimation risks over a single split procedure, and establish its principal inferential properties. Finally, our analysis reveals ways to build provably better machine learning proxies through causal learning: we can use the objective functions that we develop to construct the best linear predictors of the effects, to obtain better machine learning proxies in the initial step. We illustrate the use of both inferential tools and causal learners with a randomized field experiment that evaluates a combination of nudges to stimulate demand for immunization in India.