Treatment Selection Bias

Treatment selection bias, a pervasive issue in observational studies, arises when treatment assignment is not random, leading to inaccurate estimations of treatment effects. Current research focuses on mitigating this bias using various techniques, including propensity score methods, representation balancing (often employing deep learning architectures like recurrent neural networks and optimal transport methods), and counterfactual estimation. These advancements aim to improve the accuracy and reliability of causal inference from observational data, with significant implications for personalized medicine, policy evaluation, and other fields relying on real-world data analysis.