In recent years, real-world external controls (ECs) have gained popularity to enhance the efficacy of randomized controlled trials (RCTs), particularly in scenarios involving rare diseases or situations where equitable randomization is unfeasible or unethical. However, the suitability of ECs compared to RCTs varies, necessitating cautious consideration before utilizing ECs to avoid introducing substantial bias into treatment effect estimation. A central challenge lies in the potential incongruity of outcomes between concurrent controls (CCs) and ECs, even after accounting for covariate disparities, often attributable to latent confounding variables. This talk delves into a range of methodologies designed to mitigate the unknown biases associated with ECs. These methodologies encompass pre-testing, bias function modeling, and selective borrowing, all framed within the context of semiparametric models. These proposed strategies collectively form an essential toolkit for practitioners aiming to incorporate ECs effectively, offering a comprehensive framework to navigate their integration.