Sampling is informative when probabilities of sample inclusion depend on unknown variables that are correlated with a response variable of interest. When sample inclusion probabilities are available, inverse probability weighting can be used to account for informative sampling in such a situation, although usually at the cost of less precise inference. This paper reviews two important research contributions by Chris Skinner that modify these weights to reduce their variability while at the same time retaining consistency of the weighted estimators. In some cases, however, sample inclusion probabilities are not known, and are estimated as propensity scores. This is often the situation in causal analysis, and double robust methods that protect against the resulting misspecification of the sampling process have been the focus of much recent research. In this paper we propose two model-assisted modifications to the popular inverse propensity score weighted estimator of an average treatment effect, and then illustrate their use in a causal analysis of a rainfall enhancement experiment that was carried out in Oman between 2013 and 2018.

中文翻译：

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加权、信息量和因果推理，应用于降雨增强

当样本包含的概率取决于与感兴趣的响应变量相关的未知变量时，抽样是提供信息的。当样本包含概率可用时，可以使用逆概率加权来解释这种情况下的信息抽样，尽管通常以不太精确的推理为代价。本文回顾了 Chris Skinner 的两项重要研究贡献，它们修改了这些权重以减少它们的可变性，同时保持加权估计量的一致性。然而，在某些情况下，样本包含概率是未知的，并且被估计为倾向得分。这通常是因果分析中的情况，防止由此导致的抽样过程错误指定的双重稳健方法已成为近期研究的重点。