There is considerable interest in studying the impact of major life events (e.g., marriage, job loss) on people’s lives. This line of research is inherently causal: Its goal is to study whether life events cause changes in the examined outcomes. However, because major life events cannot be randomly assigned, studies in this area necessarily rely on longitudinal observational data. In this article, we provide guidelines for researchers interested in studying life events in an explicitly causal framework. Although focused on life-event studies for substantive context, many recommendations also apply to longitudinal observational studies more broadly. We begin by emphasizing the importance of clearly specifying the causal estimand and describe conditions in which the defined causal estimand can be identified. Then, we discuss the features and challenges of the two main analytical approaches to causal inference in life-event studies: difference-in-difference designs with a (matched) comparison group that attempt to separate event-related changes from normative changes and within-person designs that control for all time-invariant person-level confounders. We describe how the desired causal effect can be estimated in these designs and provide recommendations for when to apply each modeling strategy. In addition, we present methods for conducting sensitivity analysis, probing the robustness of the estimated causal effects, and evaluating the generalizability of the results. We conclude by describing how new specialized panel studies can be designed to examine the impact of various life events in more controlled settings.
