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This paper conducts a causal-inspired analysis of automated prompt optimization across frameworks, LLMs, and tasks, identifying that specific edit types (e.g., complexity-increasing, meta-instructional) have systematic negative or positive effects depending on task characteristics, explaining generalization failures.