Measurement Error

Measurement error, the discrepancy between observed and true values, significantly impacts the reliability and validity of scientific findings and machine learning models. Current research focuses on developing methods to detect, quantify, and mitigate the effects of measurement error in various contexts, including causal inference, bias correction in high-stakes decision-making algorithms (like those using large language models), and hypothesis testing in high-dimensional data. This work often involves adapting existing statistical techniques, such as propensity score matching and kernel methods, or developing novel metrics and algorithms to address the challenges posed by measurement error in diverse data types and model architectures. Addressing measurement error is crucial for improving the accuracy and fairness of models across numerous scientific disciplines and real-world applications.