论文标题
部分可观测时空混沌系统的无模型预测
Causal Inference under Outcome-Based Sampling with Monotonicity Assumptions
论文作者
论文摘要
我们研究病例对照和病例人口采样下的因果推断。具体而言，我们关注二进制结果和二进制处理情况，其中感兴趣的参数是通过潜在结果框架定义的因果相对和可归因的风险。结果表明，强大的无视性并不总是像随机抽样下的强大，并且某些单调性假设在尖锐的识别间隔方面产生了可比的结果。具体而言，在单调治疗响应和单调治疗选择假设下，通常情况下，通常的优势比在因果相对风险上被识别为尖锐的上限。我们提供算法来推断在协变量的真实种群分布中汇总的因果参数。我们通过研究三个经验例子来展示我们的方法的实用性：在巴基斯坦享有声望的大学上私立学校的好处；留在学校和参与巴西贩毒的帮派之间的关系；以及在美国的小组练习时间和小组练习之间的联系。
We study causal inference under case-control and case-population sampling. Specifically, we focus on the binary-outcome and binary-treatment case, where the parameters of interest are causal relative and attributable risks defined via the potential outcome framework. It is shown that strong ignorability is not always as powerful as it is under random sampling and that certain monotonicity assumptions yield comparable results in terms of sharp identified intervals. Specifically, the usual odds ratio is shown to be a sharp identified upper bound on causal relative risk under the monotone treatment response and monotone treatment selection assumptions. We offer algorithms for inference on the causal parameters that are aggregated over the true population distribution of the covariates. We show the usefulness of our approach by studying three empirical examples: the benefit of attending private school for entering a prestigious university in Pakistan; the relationship between staying in school and getting involved with drug-trafficking gangs in Brazil; and the link between physicians' hours and size of the group practice in the United States.
