Unmeasured Confounding

Unmeasured confounding, the presence of hidden variables influencing both treatment and outcome, poses a significant challenge to causal inference, hindering accurate estimation of causal effects from observational data. Current research focuses on developing methods to identify and mitigate this confounding, employing techniques like latent variable models, recursive algorithms based on higher-order cumulants, and sensitivity analyses that quantify the impact of unobserved confounders. These advancements are crucial for improving the reliability of causal inferences across diverse fields, from healthcare and social sciences to machine learning and policy evaluation, where observational data are often the primary source of information.