Unbiased Estimator

Unbiased estimators aim to provide accurate estimations of parameters without systematic error, a crucial goal across diverse scientific fields. Current research focuses on developing such estimators within various contexts, including regression analysis (using stochastic gradient descent and randomized multilevel Monte Carlo), off-policy learning (leveraging control variates and optimizing for minimal variance), and handling challenges like delayed feedback and unobserved confounding (employing techniques like inverse propensity scoring and multiply robust machine learning). The development of robust and efficient unbiased estimators significantly impacts the reliability of statistical inferences and improves the accuracy of predictions in machine learning, causal inference, and other data-driven applications.