Counterfactual Learning

Counterfactual learning aims to estimate the outcomes of interventions or actions that did not actually occur, using observational data. Current research focuses on mitigating biases in this estimation, particularly in applications like personalized medicine and ranking systems, employing techniques like inverse propensity scoring and proximal ranking policy optimization to improve robustness and safety. These advancements are significant for various fields, enabling more reliable causal inference and improved decision-making in areas such as precision medicine, recommendation systems, and even gait recognition, by accounting for confounding factors and ensuring reliable predictions.