Estimation Bias

Estimation bias, the systematic difference between an estimated value and its true value, is a pervasive problem across diverse fields of machine learning and statistics. Current research focuses on developing methods to mitigate this bias in various contexts, including reinforcement learning (e.g., through techniques like twin delayed deep deterministic policy gradient and adaptive ensemble Q-learning), causal inference (using inverse probability weighting and counterfactual approaches), and high-dimensional data analysis (employing techniques like moment-constrained learning and Pareto-smoothed weighting). Addressing estimation bias is crucial for improving the reliability and accuracy of models and algorithms across numerous applications, ranging from medical diagnosis to economic forecasting and autonomous systems.