Observational Study

Observational studies, which analyze naturally occurring data without experimental manipulation, are crucial for investigating phenomena where randomized controlled trials are infeasible or unethical. Current research focuses on mitigating biases inherent in observational data, particularly confounding, through advanced statistical techniques like double machine learning and propensity score methods, as well as leveraging machine learning models to predict counterfactuals and estimate treatment effects. These advancements are improving the reliability and generalizability of observational studies, impacting diverse fields from medicine and public health to social sciences and software engineering by providing valuable insights where experimental data are limited.