Identification and multiply robust estimation of causal effects via instrumental variables from an auxiliary population
利用辅助总体工具变量识别因果效应与多重稳健估计
Estimating causal effects in a target population with unmeasured confounders is challenging, especially when instrumental variables (IVs) are unavailable. However, IVs from auxiliary populations with similar problems can help infer causal effects in the target population. While the homogeneous conditional average treatment effect assumption has been widely used for effect transportability, it has not been explored in IV-based data fusion. We include it as a basic approach,…