Counterfactual Inference

Counterfactual inference aims to answer "what if" questions by estimating outcomes under hypothetical interventions, a crucial task for causal understanding and decision-making across diverse fields. Current research emphasizes developing robust methods to handle confounding variables, both observed and unobserved, often employing deep learning architectures like variational autoencoders, generative adversarial networks, and normalizing flows, alongside causal graphical models and quantile regression techniques. This work holds significant importance for improving the reliability of causal inferences and enabling data-driven decision-making in areas such as healthcare, policy analysis, and industrial process optimization.