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Introduces Evolutionary Feature Engineering (EFE), a framework that uses LLM-based evolution to automatically discover preprocessing transformations for structured data, improving time-series forecasting and tabular prediction accuracy while preserving interpretability.
This paper evaluates the practical effectiveness of Markov boundaries for tabular prediction, finding that while theoretically optimal, current causal discovery methods fail to consistently improve predictive performance due to computational limitations and mismatched optimization goals.