The article discusses how to accurately determine the effects of interventions in complex systems using causal models. By assuming linear causal relationships with equal error variances, researchers can estimate causal effects and confidence intervals. However, when the causal structure is unknown, traditional methods may lead to overly optimistic results. The study introduces a framework that considers uncertainty in both the causal structure and the size of effects, providing more reliable conclusions about causal effects.