Counterfactual Classification

Counterfactual classification aims to predict outcomes under hypothetical scenarios, differing from observed reality in one or more features. Current research focuses on developing robust and accurate methods for generating these counterfactual predictions, particularly addressing challenges in text classification and graph-structured data, often leveraging causal inference frameworks and techniques like self-training or doubly robust estimation to mitigate bias and improve model performance. This field is significant for enhancing model explainability, mitigating bias in decision-making systems, and enabling more informed choices in diverse applications such as recidivism prediction and personalized medicine.