The article discusses how we learn about cause and effect relationships. It introduces a new way to understand if a cause leads to an effect (causal structure) and how strong that connection is (causal strength). The new model, causal support, predicts important patterns in how we make these judgments. It explains things like how likely an effect is without the cause, how sample size affects our conclusions, and how we learn from different rates. Causal support seems to do a better job at explaining these patterns than other models.