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
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…