Sample selection bias is a well-known problem in machine learning, where the source and target data distributions differ, leading to biased predictions and difficulties in generalization. This bias presents significant challenges for modern machine learning algorithms. To tackle this problem, researchers have focused on developing domain adaptation techniques that aim to create robust methods against sample selection bias. One approach is the use of minimax estimation techniques, which belong to the category of inference-based techniques. Despite the extensive research in developing these domain adaptation methods, there remains a critical need to evaluate their performance. This thesis explores the performance differences of various minimax estimation techniques in the presence of sample selection bias, providing insights into their effectiveness in mitigating the challenges posed by biased data. By understanding and evaluating the performance of these techniques, this research contributes to the advancement of domain adaptation methods and their application in real-world machine learning scenarios.