This question seeks to understand the difficulties that arise when combining data-driven machine learning methods with traditional, theory-based economic models in cost-effectiveness analysis. How can we maintain interpretability and theoretical grounding while using machine learning? What are the trade-offs, and how can domain knowledge be integrated into machine learning models to ensure meaningful results, especially when facing constraints like limited data or uncertainty?
Integrating machine learning with traditional economic modeling in cost-effectiveness analysis presents challenges like interpretability, overfitting, incompatibility with causal inference frameworks, and difficulty incorporating economic constraints or theoretical assumptions into black-box models.
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