Adjusting for Unmeasured Confounding in Marginal Structural Models with Propensity-Score Fixed Effects
利用倾向得分固定效应调整边际结构模型中的未测混杂
Marginal structural models are a popular tool for investigating the effects of time-varying treatments, but they require an assumption of no unobserved confounders between the treatment and outcome. With observational data, this assumption may be difficult to maintain, and in studies with panel data, many researchers use fixed effects models to purge the data of time-constant unmeasured confounding. Unfortunately, traditional linear fixed effects models are not suitable for…