Nuisance Parameter

Nuisance parameters represent unwanted variables influencing observed data in statistical modeling and causal inference, obscuring the estimation of parameters of interest. Current research focuses on developing robust estimation methods, often employing techniques like doubly robust estimation, targeted minimum loss-based estimation, and orthogonalization to mitigate the impact of imprecisely estimated nuisance parameters. These advancements leverage machine learning models, including neural networks and tree ensembles, to improve efficiency and reduce bias, particularly in complex settings with high-dimensional data or networked interference. The resulting improvements in accuracy and reliability of causal effect estimates have significant implications for various fields, including healthcare, economics, and social sciences.