An experiment changes model size, preprocessing and prompts simultaneously. Why is attribution difficult?

1. Model accuracy cannot change when prompts change
2. Multiple changed factors confound the cause of improvement
3. The test dataset necessarily becomes larger
4. More factors always prove stronger causality