Latent Confounding

Latent confounding, the presence of unobserved variables influencing both treatment and outcome, poses a significant challenge to causal inference across diverse fields. Current research focuses on developing methods to mitigate this bias, employing techniques like instrumental variables, graph neural networks, and novel algorithms for causal discovery and effect estimation in both linear and non-linear settings, often incorporating machine learning models such as large language models and recurrent neural networks. Addressing latent confounding is crucial for improving the validity of causal conclusions drawn from observational studies, impacting fields ranging from medicine and social sciences to climate science and algorithmic fairness.