Summary:
- This article discusses the importance of causal models in predictive analytics. It explains that while predictive models can make accurate predictions, they may not be able to explain the underlying causes of the predictions.
- The article suggests that causal models, which focus on understanding the relationships between variables, can provide more meaningful insights and help make better decisions.
- It also highlights the limitations of purely predictive models and the need to incorporate causal reasoning into the modeling process to gain a deeper understanding of the problem at hand.