Counterfactual State

Counterfactual states represent hypothetical scenarios where one or more factors differ from reality, enabling investigation of causal relationships and model behavior. Current research focuses on generating and evaluating these states, particularly within machine learning contexts, using methods like contrastive learning, causal inference frameworks (e.g., Pearl's Causal Hierarchy), and goal-directed programming. This work aims to improve model explainability, robustness, and fairness, with applications ranging from medical diagnosis and loan approvals to natural language processing and news representation. The ultimate goal is to leverage counterfactual reasoning for more transparent, reliable, and ethically sound decision-making systems.