It is often assumed that one of the main features of a longitudinal study is the fact that causality can be detected. This is, however, only partly true for observational longitudinal studies. In Chapter 6, several methods are discussed, which claim to detect causal relationships. In a time-lag model, the covariate measured at a certain time-point is related to the outcome measured one time-point further. Because of the temporality between the outcome and the covariate, the observed relationship is assumed to be causal. A relatively new method to detect causal relationships in longitudinal studies is longitudinal mediation analysis. In this chapter, several longitudinal mediation models are discussed. In the last part of this chapter, some (more) sophisticated methods that claim to estimate causal relationships (i.e. G-methods and joint models) are examined. All methods are accompanied by extensive real-life data examples.