Modeling Assumption

Modeling assumptions are the underlying premises upon which models and algorithms are built, significantly impacting their accuracy, reliability, and generalizability. Current research focuses on identifying and mitigating the effects of these assumptions across diverse fields, including causal inference, machine learning, and robotics, often employing techniques like structural equation modeling and double machine learning to assess their validity. A better understanding of modeling assumptions is crucial for improving the robustness and trustworthiness of AI systems and for advancing the scientific rigor of various disciplines.