Self Selection

Self-selection bias arises when individuals strategically choose which data points to contribute, leading to skewed datasets and inaccurate models. Current research focuses on developing algorithms that account for this bias, particularly in linear regression and classification settings, often employing techniques like differentiable frameworks and second-order cone programming to mitigate the effects of strategic participation or data selection. Addressing self-selection bias is crucial for improving the reliability and validity of machine learning models across diverse applications, from econometrics and treatment effect estimation to the analysis of user behavior in online systems.