Direct and indirect treatment effects–causal chains and mediation analysis with instrumental variables Series B Statistical methodology
本文研究如何用工具变量非参数识别二元处理变量的直接和间接效应，其中间接效应通过中介变量传递，适用于连续或离散的中介变量和工具变量，并提供了模拟和实证例子。
The paper discusses the non‐parametric identification of causal direct and indirect effects of a binary treatment based on instrumental variables. We identify the indirect effect, which operates through a mediator (i.e. intermediate variable) that is situated on the causal path between the treatment and the outcome, as well as the unmediated direct effect of the treatment by using distinct instruments for the endogenous treatment and the endogenous mediator. We examine various settings to obtain non‐parametric identification of (natural) direct and indirect as well as controlled direct effects for continuous and discrete mediators and continuous and discrete instruments. We also provide a simulation study and two empirical illustrations.