Generalizing treatment effects with incomplete covariates: identifying assumptions and multiple imputation algorithms
协变量不完整时的处理效应推广：识别假设与多重插补算法AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要围绕随机试验效应向目标人群的推广、缺失协变量和因果可识别性展开，因此归入因果推断，未提供面板结构依据。
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协变量不完整时的处理效应推广：识别假设与多重插补算法
- 研究问题
- 当随机试验和目标人群观测数据均有协变量缺失，且处理和结果只在试验中观测时，如何把试验因果效应推广到目标人群？
- 方法与思路
- 作者比较分别插补、含来源固定效应的联合插补及忽略来源的联合插补，并提出将不完整协变量视为半离散变量的直接估计。
- 材料陈述的贡献
- 摘要称，方法依赖不同的缺失与识别假设；模拟及创伤患者应用表明，缺失处理方式可能改变从试验推广到目标人群的效应结论。
- 阅读关注点
- 应核对因果效应可推广性与缺失机制的假设如何组合、不同插补策略怎样利用来源信息，以及缺失处理是否改变目标参数。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已通过同行评审或获期刊接收；摘要未列出各策略的完整假设及应用效应数值，不能判断哪种普遍更优。
原文摘要
We focus on the problem of generalizing a causal effect estimated on a randomized controlled trial (RCT) to a target population described by a set of covariates from observational data. Available methods such as inverse propensity sampling weighting are not designed to handle missing values, which are however common in both data sources. In addition to coupling the assumptions for causal effect identifiability and for the missing values mechanism and to defining appropriate estimation strategies, one difficulty is to consider the specific structure of the data with two sources and treatment and outcome only available in the RCT. We propose three multiple imputation strategies to handle missing values when generalizing treatment effects, each handling the multi-source structure of the problem differently (separate imputation, joint imputation with fixed effect, joint imputation ignoring source information). As an alternative to multiple imputation, we also propose a direct estimation approach that treats incomplete covariates as semi-discrete variables. The multiple imputation strategies and the latter alternative rely on different sets of assumptions concerning the impact of missing values on identifiability. We discuss these assumptions and assess the methods through an extensive simulation study. This work is motivated by the analysis of a large registry of over 20,000 major trauma patients and an RCT studying the effect of tranexamic acid administration on mortality in major trauma patients admitted to ICU. The analysis illustrates how the missing values handling can impact the conclusion about the effect generalized from the RCT to the target population.