Individual Treatment Effect: Prediction Intervals and Sharp Bounds
个体处理效应的预测区间与锐界AI 解读 ↓
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个体处理效应的预测区间与锐界
- 研究问题
- 即使拥有大型随机实验，潜在结果联合分布仍仅部分识别时，个体处理效应能获得多有信息的预测区间，何时能够排除零？
- 方法与思路
- 作者要求预测区间对所有与观测数据相容的潜在结果联合分布有效，从二元处理和结局出发，并扩展至连续及有序结局。
- 材料陈述的贡献
- 摘要称，论文刻画个体效应预测区间并推导其概率质量函数的锐界，说明其与平均效应置信区间及两类零假设的区别。
- 阅读关注点
- 应核对预测区间的覆盖对象、锐界的可达性及排除零的条件，并检查连续结局扩展和 Fisher、Neyman 零假设比较的逻辑。
- 阅读边界
- 这是 arXiv 预印本，同行评审或接收未核实；摘要未给出锐界公式，平均效应显著不能据此推出特定个体受益或个体区间收缩。
原文摘要
Individual treatment effect (ITE) is often regarded as the ideal target of inference in causal analyses and has been the focus of several recent studies. In this paper, we describe the intrinsic limits regarding what can be learned concerning ITEs given data from large randomized experiments. We consider when a valid prediction interval for the ITE is informative and when it can be bounded away from zero. The joint distribution over potential outcomes is only partially identified from a randomized trial. Consequently, to be valid, an ITE prediction interval must be valid for all joint distribution consistent with the observed data and hence will in general be wider than that resulting from knowledge of this joint distribution. We characterize prediction intervals in the binary treatment and outcome setting, and extend these insights to models with continuous and ordinal outcomes. We derive sharp bounds on the probability mass function (pmf) of the individual treatment effect (ITE). Finally, we contrast prediction intervals for the ITE and confidence intervals for the average treatment effect (ATE). This also leads to the consideration of Fisher versus Neyman null hypotheses. While confidence intervals for the ATE shrink with increasing sample size due to its status as a population parameter, prediction intervals for the ITE generally do not vanish, leading to scenarios where one may reject the Neyman null yet still find evidence consistent with the Fisher null, highlighting the challenges of individualized decision-making under partial identification.