Counterfactual Model

Counterfactual models aim to answer "what if" questions by simulating alternative scenarios based on observed data, enabling causal inference and improved decision-making. Current research focuses on developing robust methods for constructing these models, particularly within graphical causal frameworks and using machine learning techniques like generative models and quantile regression, to address limitations in existing approaches such as potential outcome and structural causal models. This work is crucial for enhancing the explainability and reliability of AI systems, particularly in high-stakes domains like healthcare, and for advancing our understanding of causal relationships in complex systems.