Extreme events in complex dynamical systems may arise from distinct mechanisms, but identifying their drivers and determining whether models represent the relevant pathways remain major challenges. This talk presents a unified information-propagation perspective on these two questions. First, assimilative causal inference combines causal diagnostics with data assimilation to identify the variables and dynamical pathways that contribute to an extreme event and to quantify their ranges of influence. Examples involving both noise-driven and nonlinearity-driven extremes illustrate how different mechanisms produce distinct causal signatures and precursor structures. Second, controlled state reconstruction is used to interrogate whether a dynamical model transmits observational information toward an extreme-event target through physically meaningful pathways. The resulting reconstruction fingerprints reveal not only whether additional observations improve the representation of an extreme, but also where information transfer is distorted or suppressed. Applications to ENSO extremes in CMIP models examine the coupled pathways linking sea-surface temperature, winds, thermocline variability, and ocean recharge. These methods aim to move beyond predicting extreme events toward understanding their mechanisms and assessing whether a model’s explanation of them can be trusted.