An explanation tool identifies features contributing to a particular prediction. Which claim should be avoided?

1. Using explanations to inspect unexpected model dependence
2. Treating a model explanation as proof of a causal real-world effect
3. Comparing explanation patterns across segments
4. Checking explanations together with data quality