aiqbench analysis

This article addresses a critical methodological challenge in causal inference, explaining why high-performing predictive models often fail when estimating treatment effects due to their tendency to omit confounding variables. It proposes Bayesian Adjustment for Confounding as a rigorous alternative framework to correct these biases and improve the validity of AI-driven intervention analysis. This content is essential for practitioners aiming to bridge the gap between pure prediction accuracy and actionable causal insights in machine learning applications.