Instrumental Variable

Instrumental variable (IV) methods are statistical techniques used to estimate causal effects when confounding variables obscure the true relationship between treatment and outcome. Current research focuses on addressing limitations of traditional IV approaches, particularly in high-dimensional data and scenarios with hidden confounders, employing techniques like graph neural networks, variational autoencoders, and double machine learning to improve estimation accuracy and robustness. These advancements are significantly impacting fields such as personalized medicine, recommender systems, and econometrics by enabling more reliable causal inference from observational data where randomized controlled trials are infeasible or impractical. The development of data-driven methods for IV selection and the integration of machine learning algorithms are key trends driving progress in this area.