Social epidemiology aims to identify social structural risk factors thus informing targets and timing of interventions. Ascertaining which interventions will be most effective and when they should be implemented is challenging because social conditions vary across the life-course and are subject to time-varying confounding. Marginal Structural Models (MSMs) methods may be useful but can present unique challenges when studying social epidemiologic exposures over the life-course. We describe selected MSMs corresponding with common theoretical life-course models and identify key issues for consideration, related to time-varying confounding and late-study enrollment. Using simulated data mimicking a cohort study evaluating the effects of depression in early-, mid-, and late-life on late-life stroke risk, we examined whether and when specific study characteristics and analytic strategies may induce bias. In the context of time-varying confounding, inverse probability weighted estimation of correctly specified MSMs accurately estimated the target causal effects, while conventional regression models showed significant bias. When no measure of early-life depression is available, neither MSMs nor conventional models were unbiased, due to confounding by early-life depression. To inform interventions, researchers need to identify timing of effects and consider if missing data regarding exposures earlier in life may lead to biased estimates.