Conditional Quantiles

Conditional quantile estimation focuses on predicting not just the average value of a variable given certain conditions, but its entire conditional distribution, represented by quantiles. Current research emphasizes developing robust and efficient methods for high-dimensional data, often employing techniques like quantile regression with penalized methods (e.g., Lasso) or advanced machine learning models such as XGBoost and neural networks, sometimes incorporating Bayesian or conformal prediction frameworks to ensure reliable uncertainty quantification. These advancements are proving valuable in diverse fields, improving prediction accuracy and providing richer insights in applications ranging from finance and healthcare to recommendation systems and causal inference.